diff --git a/.gitignore b/.gitignore index a25ef5f..46e4914 100644 --- a/.gitignore +++ b/.gitignore @@ -74,4 +74,6 @@ scripts/.venv/ # Mnemonic secrets (use mnemonics.example.json as template) mnemonics.local.json -./claude/worktrees/* \ No newline at end of file +./claude/worktrees/* +# Python bytecode +scripts/__pycache__/ diff --git a/README.md b/README.md index cf48743..87354c7 100644 --- a/README.md +++ b/README.md @@ -106,9 +106,8 @@ yarn setup:ocr # Download OCR models python3 - <<'PY' from huggingface_hub import snapshot_download -snapshot_download('PaddlePaddle/en_PP-OCRv5_mobile_rec', local_dir='models/paddle_ocr/en_PP-OCRv5_mobile_rec') -snapshot_download('PaddlePaddle/PP-OCRv5_mobile_det', local_dir='models/paddle_ocr/PP-OCRv5_mobile_det') -snapshot_download('PaddlePaddle/PP-OCRv5_mobile_rec', local_dir='models/paddle_ocr/PP-OCRv5_mobile_rec') +snapshot_download('PaddlePaddle/PP-OCRv6_medium_rec_onnx', local_dir='models/paddle_ocr/PP-OCRv6_medium_rec_onnx') +snapshot_download('PaddlePaddle/PP-OCRv6_medium_det_onnx', local_dir='models/paddle_ocr/PP-OCRv6_medium_det_onnx') print('done') PY @@ -131,8 +130,8 @@ yarn build:linux # Linux QA Auto Hardware uses a dual-path OCR pipeline for hardware wallet mnemonic capture: -- **Mnemonic display page & confirmation option area**: `en_PP-OCRv5_mobile_rec` -- **Confirmation page question number area (`#N`)**: `PP-OCRv5_mobile_det` + `PP-OCRv5_mobile_rec` +- **Mnemonic display page & confirmation option area**: `PP-OCRv6_medium_rec_onnx` +- **Confirmation page question number area (`#N`)**: `PP-OCRv6_medium_det_onnx` + `PP-OCRv6_medium_rec_onnx` **Typical workflow:** @@ -145,9 +144,16 @@ QA Auto Hardware uses a dual-path OCR pipeline for hardware wallet mnemonic capt | Model | Environment Variable Override | |-------|-------------------------------| -| `en_PP-OCRv5_mobile_rec` | `QA_AUTO_HW_OCR_MODEL_DIR` | -| `PP-OCRv5_mobile_det` | `QA_AUTO_HW_OCR_DET_MODEL_DIR` | -| `PP-OCRv5_mobile_rec` | `QA_AUTO_HW_OCR_MULTI_REC_MODEL_DIR` | +| `PP-OCRv6_medium_rec_onnx` | `QA_AUTO_HW_OCR_MODEL_DIR` | +| `PP-OCRv6_medium_det_onnx` | `QA_AUTO_HW_OCR_DET_MODEL_DIR` | + +To try the smaller PP-OCRv6 small models: + +```bash +QA_AUTO_HW_OCR_MODEL_DIR=models/paddle_ocr/PP-OCRv6_small_rec_onnx \ +QA_AUTO_HW_OCR_DET_MODEL_DIR=models/paddle_ocr/PP-OCRv6_small_det_onnx \ +yarn electron:dev +``` Optional tuning: `QA_AUTO_HW_OCR_MAX_IMAGE_SIDE` (default 1280), `QA_AUTO_HW_OCR_CPU_THREADS` (default 4) diff --git a/electron/main.ts b/electron/main.ts index e2176cb..5bf6035 100644 --- a/electron/main.ts +++ b/electron/main.ts @@ -666,7 +666,7 @@ ipcMain.handle( ); /** - * IPC handler: Runs en_PP-OCRv5_mobile_rec inference in a Python subprocess. + * IPC handler: Runs PP-OCRv6 small OCR inference in a Python subprocess. */ ipcMain.handle( 'paddleocr-en-recognize', @@ -677,6 +677,7 @@ ipcMain.handle( layoutHint?: 'mnemonic' | 'verify-options' | 'verify-number' | 'generic'; expectedWordCount?: number; recVariantCount?: number; + wordlistHint?: 'bip39' | 'slip39'; } ) => runPaddleOcrEn(payload) ); diff --git a/electron/mcp/sequences.ts b/electron/mcp/sequences.ts index 07f9a00..e5a2bbe 100644 --- a/electron/mcp/sequences.ts +++ b/electron/mcp/sequences.ts @@ -316,12 +316,13 @@ const ALL_PAGE_ACTIONS: PageAction[] = [ name: '输入PIN码1111', group: '初始设置', steps: [ - { label: '输入PIN码1', x: 25, y: 50, depth: 12 }, + // Wait for the PIN pad to finish rendering before the first tap. + { label: '输入PIN码1', x: 25, y: 50, depth: 12, delayBefore: 500 }, { label: '输入PIN码2', x: 25, y: 50, depth: 12 }, { label: '输入PIN码3', x: 25, y: 50, depth: 12 }, { label: '输入PIN码4', x: 25, y: 50, depth: 12 }, { label: '点击确认', x: 55, y: 85, depth: 12 }, - { label: '再次确认PIN码1', x: 25, y: 50, depth: 12 }, + { label: '再次确认PIN码1', x: 25, y: 50, depth: 12, delayBefore: 500 }, { label: '再次确认PIN码2', x: 25, y: 50, depth: 12 }, { label: '再次确认PIN码3', x: 25, y: 50, depth: 12 }, { label: '再次确认PIN码4', x: 25, y: 50, depth: 12 }, diff --git a/electron/mcp/state.ts b/electron/mcp/state.ts index cc95a39..6b000cf 100644 --- a/electron/mcp/state.ts +++ b/electron/mcp/state.ts @@ -70,6 +70,7 @@ export interface MnemonicOcrRequest { allowPartial?: boolean; requireBip39?: boolean; sceneConfig?: SequenceOcrSceneConfig; + deviceTestSetId?: 'pro' | 'pro2'; } export interface MnemonicStoreMetadata { diff --git a/electron/mcp/tools/executeSequence.ts b/electron/mcp/tools/executeSequence.ts index ae24fed..740ffc4 100644 --- a/electron/mcp/tools/executeSequence.ts +++ b/electron/mcp/tools/executeSequence.ts @@ -28,6 +28,7 @@ import { getPageAction, getSequence, normalizeDeviceTestSetId, + type DeviceTestSetId, } from '../sequenceSets'; import { resolvePageActionStepsForDevice } from '../sequenceSetResolver'; import { saveCaptureToDownloads } from '../../saveCapture'; @@ -90,6 +91,7 @@ function requiresPostSequenceCooldown(sequence: { category: string; actions: str async function executeStep( step: AutoStep, resourceHandle: number, + deviceTestSetId: DeviceTestSetId, httpRequest: (url: string) => Promise ): Promise { if ((step.delayBefore ?? 0) > 0) { @@ -114,7 +116,10 @@ async function executeStep( // Give the device screen time to finish inertial scrolling before capture. await delay(1600); - const ocrResult = await runMnemonicOcr(ocrCaptureConfig); + const ocrResult = await runMnemonicOcr({ + ...ocrCaptureConfig, + deviceTestSetId, + }); if (!ocrResult) { throw new Error('Renderer did not return mnemonic OCR result'); } @@ -327,7 +332,7 @@ export async function executeExecuteSequence( }; } - await executeStep(step, state.resourceHandle, httpRequest); + await executeStep(step, state.resourceHandle, deviceTestSetId, httpRequest); stepsCompleted++; console.log(`[execute-sequence] Step ${stepsCompleted}/${totalSteps}: ${step.label}`); } diff --git a/electron/paddleOcrEn.ts b/electron/paddleOcrEn.ts index 57641c6..872b79d 100644 --- a/electron/paddleOcrEn.ts +++ b/electron/paddleOcrEn.ts @@ -9,12 +9,14 @@ export interface PaddleOcrEnRequest { layoutHint?: 'mnemonic' | 'verify-options' | 'verify-number' | 'generic'; expectedWordCount?: number; recVariantCount?: number; + /** Closed wordlist of the current flow (BIP39 vs SLIP39 — never mixed). */ + wordlistHint?: 'bip39' | 'slip39'; } export interface PaddleOcrEnResult { text: string; confidence: number; - backend: 'en_PP-OCRv5_mobile_rec'; + backend: 'PP-OCRv6_medium_rec_onnx'; elapsedMs: number; } @@ -34,6 +36,7 @@ const HEALTH_CACHE_TTL_MS = 30 * 1000; const REQUIRED_PYTHON_MODULES = [ 'paddleocr', 'paddle', + 'onnxruntime', 'cv2', 'PIL', 'yaml', @@ -41,19 +44,24 @@ const REQUIRED_PYTHON_MODULES = [ ] as const; const REQUIRED_MODEL_CONFIGS = [ { - name: 'en_PP-OCRv5_mobile_rec', - envKeys: ['QA_AUTO_HW_OCR_MODEL_DIR', 'QA_AUTO_HW_EN_OCR_MODEL_DIR'], + name: 'PP-OCRv6_medium_rec_onnx', + envKeys: [ + 'QA_AUTO_HW_OCR_MODEL_DIR', + 'QA_AUTO_HW_EN_OCR_MODEL_DIR', + 'QA_AUTO_HW_OCR_MULTI_REC_MODEL_DIR', + ], }, { - name: 'PP-OCRv5_mobile_rec', - envKeys: ['QA_AUTO_HW_OCR_MULTI_REC_MODEL_DIR'], + name: 'PP-OCRv6_medium_det_onnx', + envKeys: ['QA_AUTO_HW_OCR_DET_MODEL_DIR'], }, +] as const; +const REQUIRED_MODEL_FILE_CONFIGS = [ { - name: 'PP-OCRv5_mobile_det', - envKeys: ['QA_AUTO_HW_OCR_DET_MODEL_DIR'], + suffixes: ['inference.json', 'inference.onnx', 'inference.yml'], }, ] as const; -const REQUIRED_MODEL_FILES = ['inference.json', 'inference.pdiparams', 'inference.yml'] as const; +const REQUIRED_MODEL_FILES = REQUIRED_MODEL_FILE_CONFIGS[0].suffixes; let cachedHealthStatus: { expiresAt: number; value: PaddleOcrEnHealthStatus } | null = null; @@ -271,14 +279,14 @@ class PaddleOcrEnDaemon { await this.ensureStarted(); if (!this.child) { - throw new Error('PP-OCRv5 rec daemon is not running'); + throw new Error('PP-OCRv6 rec daemon is not running'); } const id = this.nextId++; return new Promise((resolve, reject) => { const timer = setTimeout(() => { this.pending.delete(id); - reject(new Error('PP-OCRv5 rec request timed out after 2 minutes')); + reject(new Error('PP-OCRv6 rec request timed out after 2 minutes')); }, 2 * 60 * 1000); this.pending.set(id, { resolve, reject, timer }); @@ -294,7 +302,7 @@ class PaddleOcrEnDaemon { } this.startPromise = null; this.stdoutBuffer = ''; - this.rejectAllPending(new Error('PP-OCRv5 rec daemon stopped')); + this.rejectAllPending(new Error('PP-OCRv6 rec daemon stopped')); } private async ensureStarted(): Promise { @@ -335,7 +343,7 @@ class PaddleOcrEnDaemon { const bootTimeout = setTimeout(() => { child.kill('SIGKILL'); - reject(new Error('Timed out while starting PP-OCRv5 rec daemon')); + reject(new Error('Timed out while starting PP-OCRv6 rec daemon')); }, 45 * 1000); child.stdout.on('data', (chunk: Buffer) => { @@ -359,14 +367,14 @@ class PaddleOcrEnDaemon { child.on('error', (err) => { clearTimeout(bootTimeout); this.child = null; - reject(new Error(`Failed to start PP-OCRv5 rec daemon: ${err.message}`)); + reject(new Error(`Failed to start PP-OCRv6 rec daemon: ${err.message}`)); }); child.on('close', (code) => { clearTimeout(bootTimeout); this.child = null; this.startPromise = null; - const reason = `PP-OCRv5 rec daemon exited with code ${code}. stderr: ${this.stderrBuffer.trim() || '(empty)'}`; + const reason = `PP-OCRv6 rec daemon exited with code ${code}. stderr: ${this.stderrBuffer.trim() || '(empty)'}`; this.rejectAllPending(new Error(reason)); }); }).finally(() => { @@ -391,6 +399,11 @@ class PaddleOcrEnDaemon { } if (parsed.type === 'ready') { + console.log( + `[paddleOcrEn] OCR server ready (script version: ${ + typeof parsed.scriptVersion === 'string' ? parsed.scriptVersion : 'pre-versioning' + })` + ); options?.onReady?.(); continue; } @@ -407,7 +420,7 @@ class PaddleOcrEnDaemon { const errorText = typeof parsed.error === 'string' ? parsed.error - : 'Unknown PP-OCRv5 rec daemon error'; + : 'Unknown PP-OCRv6 rec daemon error'; pending.reject(new Error(errorText)); continue; } @@ -415,7 +428,7 @@ class PaddleOcrEnDaemon { pending.resolve({ text: typeof parsed.text === 'string' ? parsed.text : '', confidence: typeof parsed.confidence === 'number' ? parsed.confidence : 0, - backend: 'en_PP-OCRv5_mobile_rec', + backend: 'PP-OCRv6_medium_rec_onnx', elapsedMs: typeof parsed.elapsedMs === 'number' ? parsed.elapsedMs : 0, }); } diff --git a/electron/preload.ts b/electron/preload.ts index d1086f2..2629795 100644 --- a/electron/preload.ts +++ b/electron/preload.ts @@ -67,7 +67,7 @@ interface VerifyOcrPayload { interface PaddleOcrEnPayload { text: string; confidence: number; - backend: 'en_PP-OCRv5_mobile_rec'; + backend: 'PP-OCRv6_medium_rec_onnx'; elapsedMs: number; } @@ -88,6 +88,12 @@ interface ResolvedSequenceStepPayload { mergeWithStored?: boolean; allowPartial?: boolean; requireBip39?: boolean; + sceneConfig?: { + roi: { x: number; y: number; width: number; height: number }; + scale?: number; + useNearestNeighbor?: boolean; + }; + deviceTestSetId?: 'pro' | 'pro2'; }; ocrVerify?: { options: { x: number; y: number; depth: number }[]; @@ -237,13 +243,15 @@ contextBridge.exposeInMainWorld('electronAPI', { imageDataUrl: string, layoutHint?: 'mnemonic' | 'verify-options' | 'verify-number' | 'generic', expectedWordCount?: number, - recVariantCount?: number + recVariantCount?: number, + wordlistHint?: 'bip39' | 'slip39' ) => ipcRenderer.invoke('paddleocr-en-recognize', { imageDataUrl, layoutHint, expectedWordCount, recVariantCount, + wordlistHint, }) as Promise, // MCP Log: Receive log entries from main process @@ -327,7 +335,8 @@ declare global { imageDataUrl: string, layoutHint?: 'mnemonic' | 'verify-options' | 'verify-number' | 'generic', expectedWordCount?: number, - recVariantCount?: number + recVariantCount?: number, + wordlistHint?: 'bip39' | 'slip39' ) => Promise; onMcpServerReady: (callback: (info: { port: number }) => void) => void; // MCP Logs diff --git a/electron/saveCapture.ts b/electron/saveCapture.ts index a7811cf..bf3ccae 100644 --- a/electron/saveCapture.ts +++ b/electron/saveCapture.ts @@ -30,11 +30,20 @@ function extractBase64(dataUrlOrBase64: string): string { return dataUrlOrBase64; } +/** + * Picks the file extension matching the data URL mime type (default jpg). + */ +function extensionForDataUrl(dataUrlOrBase64: string): string { + const match = dataUrlOrBase64.match(/^data:image\/(png|jpeg|webp)/); + if (!match) return 'jpg'; + return match[1] === 'jpeg' ? 'jpg' : match[1]; +} + /** * Saves a capture (data URL or raw base64) to the user's Downloads folder. - * Filename: qa-auto-hw-YYYYMMDD-HHmmss-sss-{contentHint}.jpg + * Filename: qa-auto-hw-YYYYMMDD-HHmmss-sss-{contentHint}.{ext} * - * @param dataUrlOrBase64 - JPEG as data URL or raw base64 string + * @param dataUrlOrBase64 - image as data URL or raw base64 string (assumed JPEG) * @param contentHint - Short label for the file (e.g. ocr-x85-y0, ocr-trigger) * @returns Full path of the saved file */ @@ -48,7 +57,7 @@ export async function saveCaptureToDownloads( const hhmmss = now.toTimeString().slice(0, 8).replace(/:/g, ''); const ms = now.getMilliseconds().toString().padStart(3, '0'); const hint = sanitizeHint(contentHint); - const filename = `qa-auto-hw-${dateStr}-${hhmmss}-${ms}-${hint}.jpg`; + const filename = `qa-auto-hw-${dateStr}-${hhmmss}-${ms}-${hint}.${extensionForDataUrl(dataUrlOrBase64)}`; const fullPath = path.join(downloadsDir, filename); const base64 = extractBase64(dataUrlOrBase64); diff --git a/models/paddle_ocr/PP-OCRv6_medium_det_onnx/.gitattributes b/models/paddle_ocr/PP-OCRv6_medium_det_onnx/.gitattributes new file mode 100644 index 0000000..a6344aa --- /dev/null +++ b/models/paddle_ocr/PP-OCRv6_medium_det_onnx/.gitattributes @@ -0,0 +1,35 @@ +*.7z filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text +*.bin filter=lfs diff=lfs merge=lfs -text +*.bz2 filter=lfs diff=lfs merge=lfs -text +*.ckpt filter=lfs diff=lfs merge=lfs -text +*.ftz filter=lfs diff=lfs merge=lfs -text +*.gz filter=lfs diff=lfs merge=lfs -text +*.h5 filter=lfs diff=lfs merge=lfs -text +*.joblib filter=lfs diff=lfs merge=lfs -text +*.lfs.* filter=lfs diff=lfs merge=lfs -text +*.mlmodel filter=lfs diff=lfs merge=lfs -text +*.model filter=lfs diff=lfs merge=lfs -text +*.msgpack filter=lfs diff=lfs merge=lfs -text +*.npy filter=lfs diff=lfs merge=lfs -text +*.npz filter=lfs diff=lfs merge=lfs -text +*.onnx filter=lfs diff=lfs merge=lfs -text +*.ot filter=lfs diff=lfs merge=lfs -text +*.parquet filter=lfs diff=lfs merge=lfs -text +*.pb filter=lfs diff=lfs merge=lfs -text +*.pickle filter=lfs diff=lfs merge=lfs -text +*.pkl filter=lfs diff=lfs merge=lfs -text +*.pt filter=lfs diff=lfs merge=lfs -text +*.pth filter=lfs diff=lfs merge=lfs -text +*.rar filter=lfs diff=lfs merge=lfs -text +*.safetensors filter=lfs diff=lfs merge=lfs -text +saved_model/**/* filter=lfs diff=lfs merge=lfs -text +*.tar.* filter=lfs diff=lfs merge=lfs -text +*.tar filter=lfs diff=lfs merge=lfs -text +*.tflite filter=lfs diff=lfs merge=lfs -text +*.tgz filter=lfs diff=lfs merge=lfs -text +*.wasm filter=lfs diff=lfs merge=lfs -text +*.xz filter=lfs diff=lfs merge=lfs -text +*.zip filter=lfs diff=lfs merge=lfs -text +*.zst filter=lfs diff=lfs merge=lfs -text +*tfevents* filter=lfs diff=lfs merge=lfs -text diff --git a/models/paddle_ocr/PP-OCRv6_medium_det_onnx/README.md b/models/paddle_ocr/PP-OCRv6_medium_det_onnx/README.md new file mode 100644 index 0000000..ebe7e69 --- /dev/null +++ b/models/paddle_ocr/PP-OCRv6_medium_det_onnx/README.md @@ -0,0 +1,181 @@ +--- +license: apache-2.0 +library_name: PaddleOCR +language: +- en +- zh +pipeline_tag: image-to-text +tags: +- OCR +- PaddlePaddle +- PaddleOCR +- textline_detection +- ONNX +--- + +
+ + +

+ +PP-OCRv6: From 1.5M to 34.5M Parameters, Surpassing Billion-Scale VLMs on OCR Tasks + +

+ +[![repo](https://img.shields.io/github/stars/PaddlePaddle/PaddleOCR?color=ccf)](https://github.com/PaddlePaddle/PaddleOCR) 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+[![X](https://img.shields.io/badge/X-PaddlePaddle-6080F0)](https://x.com/PaddlePaddle) +[![License](https://img.shields.io/badge/license-Apache_2.0-green)](./LICENSE) +[![Paddle 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+ +**🔥 [Official Website](https://www.paddleocr.com)** +**📝 [Technical Report](https://arxiv.org/pdf/2606.13108)** + +
+ +
+ +
+ +## PP-OCRv6 Overview + +PP-OCRv6 is a lightweight OCR system that combines architectural innovation with data-centric optimization. It redesigns the backbone, detection neck, and recognition neck around a unified MetaFormer-style building block with structural reparameterization. Three model tiers (medium, small, tiny) share the same block primitives, covering deployment scenarios from server to edge. + +### Key Features + +1. **Unified and Scalable Model Family:** A three-tier OCR model family spanning 1.5M to 34.5M parameters. PP-OCRv6_medium achieves 86.2% detection Hmean and 83.2% recognition accuracy, outperforming PP-OCRv5_server by +4.6% and +5.1% respectively. + +2. **Lightweight Architectural Innovations:** (i) LCNetV4, a MetaFormer-style lightweight backbone with structural reparameterization; (ii) RepLKFPN, a detection neck with dilated reparameterizable depthwise convolutions; (iii) EncoderWithLightSVTR, a recognition neck with local-global attention and additive skip connections. + +3. **Multi-Language and Scenario Support:** Supports 48 languages and diverse industrial scenes (digital displays, dot-matrix characters, tire prints, etc.), surpassing Qwen3-VL-235B, GPT-5.5, and Gemini-3.1-Pro with orders of magnitude fewer parameters. + + +# PP-OCRv6_medium_det + +## Introduction + +
+ + +PP-OCRv6 text detection architecture overview +
+ +PP-OCRv6_medium_det is the largest model in the PP-OCRv6 detection series developed by the PaddleOCR team. It uses LCNetV4 as the backbone and RepLKFPN as the feature pyramid neck, providing accurate text localization across diverse scenarios including handwritten, printed, rotated, curved, and artistic text in multiple languages. The model contains 15.5M parameters. The key accuracy metrics are as follows: + +| Model | Average | Handwritten CN | Handwritten EN | Printed CN | Printed EN | Traditional Chinese | Ancient Text | Japanese | Blur | Emoji | Warp | Pinyin | Artistic | Table | Rotation | Industrial | General | +| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | +| Gemini-3.1-Pro | 46.8 | 53.4 | 56.5 | 47.3 | 47.6 | 39.0 | 45.8 | 38.2 | 50.0 | 68.1 | 44.6 | 40.6 | 65.2 | 26.9 | 22.1 | 52.5 | 50.2 | +| GPT-5.5 | 45.6 | 42.4 | 58.5 | 50.2 | 51.9 | 35.0 | 26.7 | 42.0 | 49.1 | 97.5 | 37.7 | 36.3 | 52.0 | 71.0 | 10.0 | 36.2 | 32.6 | +| Qwen3-VL-235B | 38.3 | 56.5 | 66.0 | 41.7 | 37.0 | 19.3 | 13.1 | 27.0 | 38.5 | 81.2 | 28.5 | 33.0 | 68.3 | 19.6 | 2.1 | 48.4 | 32.3 | +| Kimi-K2.6 | 12.8 | 12.5 | 25.5 | 10.1 | 18.5 | 8.2 | 7.5 | 11.2 | 16.9 | 28.9 | 13.9 | 6.8 | 16.1 | 10.9 | 0.8 | 6.3 | 10.9 | +| MiniMax-M3 | 12.0 | 13.7 | 19.3 | 9.8 | 14.1 | 7.7 | 11.1 | 10.6 | 16.1 | 32.8 | 12.8 | 8.5 | 16.6 | 5.5 | 0.1 | 6.4 | 6.4 | +| PP-OCRv5_server | 81.6 | 80.3 | 84.1 | 94.5 | 91.7 | 81.5 | 67.6 | 77.2 | 90.1 | 96.2 | 87.6 | 67.1 | 67.3 | 97.1 | 80.0 | 64.3 | 79.7 | +| PP-OCRv5_mobile | 75.2 | 74.4 | 77.7 | 90.5 | 91.0 | 82.3 | 58.1 | 72.7 | 87.4 | 93.6 | 82.7 | 57.5 | 52.5 | 92.8 | 64.7 | 52.8 | 72.1 | +| **PP-OCRv6_medium** | **86.2** | **83.7** | **84.0** | **95.1** | **93.7** | **86.3** | **80.2** | **84.3** | **94.1** | **99.6** | **88.6** | **74.0** | **69.0** | **96.8** | **93.8** | **73.3** | **82.8** | +| PP-OCRv6_small | 84.1 | 80.5 | 87.1 | 94.2 | 93.6 | 85.7 | 72.6 | 82.3 | 92.6 | 99.7 | 87.6 | 69.6 | 65.3 | 95.6 | 93.7 | 67.6 | 78.2 | +| PP-OCRv6_tiny | 80.6 | 79.4 | 85.9 | 93.1 | 92.3 | 83.7 | 63.0 | 76.6 | 89.3 | 99.8 | 86.1 | 59.0 | 60.1 | 94.7 | 91.0 | 62.0 | 73.8 | + +## Quick Start + +### Installation + +```bash +# Install PaddleOCR +pip install paddleocr + +# Install ONNX Runtime +pip install onnxruntime-gpu # or onnxruntime for CPU-only +``` + +### Model Usage + +You can quickly experience the functionality with a single command: + +```bash +paddleocr text_detection \ + --model_name PP-OCRv6_medium_det \ + --engine onnxruntime \ + -i https://cdn-uploads.huggingface.co/production/uploads/681c1ecd9539bdde5ae1733c/3ul2Rq4Sk5Cn-l69D695U.png +``` + +You can also integrate the model inference of the text detection module into your project. Before running the following code, please download the sample image to your local machine. + +```python +from paddleocr import TextDetection +model = TextDetection(model_name="PP-OCRv6_medium_det", engine="onnxruntime") +output = model.predict(input="3ul2Rq4Sk5Cn-l69D695U.png", batch_size=1) +for res in output: + res.print() + res.save_to_img(save_path="./output/") + res.save_to_json(save_path="./output/res.json") +``` + + +For details about usage command and descriptions of parameters, please refer to the [Document](https://paddlepaddle.github.io/PaddleOCR/latest/en/version3.x/module_usage/text_detection.html#iii-quick-start). + +### Pipeline Usage + +The general OCR pipeline extracts text information from images. The pipeline consists of several modules: +* Document Image Orientation Classification Module (Optional) +* Text Image Unwarping Module (Optional) +* Text Line Orientation Classification Module (Optional) +* Text Detection Module +* Text Recognition Module + +Run a single command to quickly experience the OCR pipeline: + +```bash +paddleocr ocr -i https://cdn-uploads.huggingface.co/production/uploads/681c1ecd9539bdde5ae1733c/3ul2Rq4Sk5Cn-l69D695U.png \ + --text_detection_model_name PP-OCRv6_medium_det \ + --text_recognition_model_name PP-OCRv6_medium_rec \ + --engine onnxruntime \ + --use_doc_orientation_classify False \ + --use_doc_unwarping False \ + --use_textline_orientation True \ + --save_path ./output \ + --device gpu:0 +``` + +For project integration: + +```python +from paddleocr import PaddleOCR + +ocr = PaddleOCR( + text_detection_model_name="PP-OCRv6_medium_det", + text_recognition_model_name="PP-OCRv6_medium_rec", + engine="onnxruntime", + use_doc_orientation_classify=False, + use_doc_unwarping=False, + use_textline_orientation=False, +) +result = ocr.predict("./3ul2Rq4Sk5Cn-l69D695U.png") +for res in result: + res.print() + res.save_to_img("output") + res.save_to_json("output") +``` + + +For details about usage command and descriptions of parameters, please refer to the [Document](https://paddlepaddle.github.io/PaddleOCR/latest/en/version3.x/pipeline_usage/OCR.html#2-quick-start). + +## Links + +[PaddleOCR Repo](https://github.com/paddlepaddle/paddleocr) + +[PaddleOCR Documentation](https://paddlepaddle.github.io/PaddleOCR/latest/en/index.html) + +## Citation + +```bibtex +@misc{zhang2026ppocrv6, + title={PP-OCRv6: From 1.5M to 34.5M Parameters, Surpassing Billion-Scale VLMs on OCR Tasks}, + author={Yubo Zhang and Xueqing Wang and Manhui Lin and Yue Zhang and Penglongyi Deng and Ting Sun and Tingquan Gao and Zelun Zhang and Jiaxuan Liu and Changda Zhou and Hongen Liu and Suyin Liang and Cheng Cui and Yi Liu and Dianhai Yu and Yanjun Ma}, + year={2026}, + eprint={2606.13108}, + archivePrefix={arXiv}, + primaryClass={cs.CV}, + url={https://arxiv.org/abs/2606.13108}, +} +``` diff --git a/models/paddle_ocr/PP-OCRv6_medium_det_onnx/inference.json b/models/paddle_ocr/PP-OCRv6_medium_det_onnx/inference.json new file mode 100644 index 0000000..548f60c --- /dev/null +++ b/models/paddle_ocr/PP-OCRv6_medium_det_onnx/inference.json @@ -0,0 +1 @@ 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\ No newline at end of file diff --git a/models/paddle_ocr/PP-OCRv6_medium_det_onnx/inference.onnx b/models/paddle_ocr/PP-OCRv6_medium_det_onnx/inference.onnx new file mode 100644 index 0000000..0a9957c Binary files /dev/null and b/models/paddle_ocr/PP-OCRv6_medium_det_onnx/inference.onnx differ diff --git a/models/paddle_ocr/PP-OCRv6_medium_det_onnx/inference.yml b/models/paddle_ocr/PP-OCRv6_medium_det_onnx/inference.yml new file mode 100644 index 0000000..1c5c058 --- /dev/null +++ b/models/paddle_ocr/PP-OCRv6_medium_det_onnx/inference.yml @@ -0,0 +1,52 @@ +Global: + model_name: PP-OCRv6_medium_det +Hpi: + backend_configs: + paddle_infer: + trt_dynamic_shapes: &id001 + x: + - - 1 + - 3 + - 32 + - 32 + - - 1 + - 3 + - 736 + - 736 + - - 1 + - 3 + - 4000 + - 4000 + tensorrt: + dynamic_shapes: *id001 +PostProcess: + box_thresh: 0.45 + max_candidates: 3000 + name: DBPostProcess + thresh: 0.2 + unclip_ratio: 1.4 +PreProcess: + transform_ops: + - DecodeImage: + channel_first: false + img_mode: BGR + - DetLabelEncode: null + - DetResizeForTest: null + - NormalizeImage: + mean: + - 0.485 + - 0.456 + - 0.406 + order: hwc + scale: 1./255. + std: + - 0.229 + - 0.224 + - 0.225 + - ToCHWImage: null + - KeepKeys: + keep_keys: + - image + - shape + - polys + - ignore_tags diff --git a/models/paddle_ocr/PP-OCRv6_medium_rec_onnx/.gitattributes b/models/paddle_ocr/PP-OCRv6_medium_rec_onnx/.gitattributes new file mode 100644 index 0000000..a6344aa --- /dev/null +++ b/models/paddle_ocr/PP-OCRv6_medium_rec_onnx/.gitattributes @@ -0,0 +1,35 @@ +*.7z filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text +*.bin filter=lfs diff=lfs merge=lfs -text +*.bz2 filter=lfs diff=lfs merge=lfs -text +*.ckpt filter=lfs diff=lfs merge=lfs -text +*.ftz filter=lfs diff=lfs merge=lfs -text +*.gz filter=lfs diff=lfs merge=lfs -text +*.h5 filter=lfs diff=lfs merge=lfs -text +*.joblib filter=lfs diff=lfs merge=lfs -text +*.lfs.* filter=lfs diff=lfs merge=lfs -text +*.mlmodel filter=lfs diff=lfs merge=lfs -text +*.model filter=lfs diff=lfs merge=lfs -text +*.msgpack filter=lfs diff=lfs merge=lfs -text +*.npy filter=lfs diff=lfs merge=lfs -text +*.npz filter=lfs diff=lfs merge=lfs -text +*.onnx filter=lfs diff=lfs merge=lfs -text +*.ot filter=lfs diff=lfs merge=lfs -text +*.parquet filter=lfs diff=lfs merge=lfs -text +*.pb filter=lfs diff=lfs merge=lfs -text +*.pickle filter=lfs diff=lfs merge=lfs -text +*.pkl filter=lfs diff=lfs merge=lfs -text +*.pt filter=lfs diff=lfs merge=lfs -text +*.pth filter=lfs diff=lfs merge=lfs -text +*.rar filter=lfs diff=lfs merge=lfs -text +*.safetensors filter=lfs diff=lfs merge=lfs -text +saved_model/**/* filter=lfs diff=lfs merge=lfs -text +*.tar.* filter=lfs diff=lfs merge=lfs -text +*.tar filter=lfs diff=lfs merge=lfs -text +*.tflite filter=lfs diff=lfs merge=lfs -text +*.tgz filter=lfs diff=lfs merge=lfs -text +*.wasm filter=lfs diff=lfs merge=lfs -text +*.xz filter=lfs diff=lfs merge=lfs -text +*.zip filter=lfs diff=lfs merge=lfs -text +*.zst filter=lfs diff=lfs merge=lfs -text +*tfevents* filter=lfs diff=lfs merge=lfs -text diff --git a/models/paddle_ocr/PP-OCRv6_medium_rec_onnx/README.md b/models/paddle_ocr/PP-OCRv6_medium_rec_onnx/README.md new file mode 100644 index 0000000..5b53afa --- /dev/null +++ b/models/paddle_ocr/PP-OCRv6_medium_rec_onnx/README.md @@ -0,0 +1,200 @@ +--- +license: apache-2.0 +library_name: PaddleOCR +language: +- en +- zh +pipeline_tag: image-to-text +tags: +- OCR +- PaddlePaddle +- PaddleOCR +- text_recognition +--- + +
+ + +

+ +PP-OCRv6: From 1.5M to 34.5M Parameters, Surpassing Billion-Scale VLMs on OCR Tasks + +

+ +[![repo](https://img.shields.io/github/stars/PaddlePaddle/PaddleOCR?color=ccf)](https://github.com/PaddlePaddle/PaddleOCR) +[![HuggingFace](https://img.shields.io/badge/HuggingFace-black.svg?logo=data:image/png;base64,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&labelColor=white)](https://huggingface.co/PaddlePaddle/PP-OCRv6_medium_rec_onnx) 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Model](https://img.shields.io/badge/Paddle_Model-0053CC.svg?logo=data:image/png;base64,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&labelColor=white)](https://huggingface.co/PaddlePaddle/PP-OCRv6_medium_rec) 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Model](https://img.shields.io/badge/Safetensors_Model-black.svg?logo=data:image/png;base64,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&labelColor=white)](https://huggingface.co/PaddlePaddle/PP-OCRv6_medium_rec_safetensors) + +**🔥 [Official Website](https://www.paddleocr.com)** +**📝 [Technical Report](https://arxiv.org/pdf/2606.13108)** + +
+ +
+ +
+ +## PP-OCRv6 Overview + +PP-OCRv6 is a lightweight OCR system that combines architectural innovation with data-centric optimization. It redesigns the backbone, detection neck, and recognition neck around a unified MetaFormer-style building block with structural reparameterization. Three model tiers (medium, small, tiny) share the same block primitives, covering deployment scenarios from server to edge. + +### Key Features + +1. **Unified and Scalable Model Family:** A three-tier OCR model family spanning 1.5M to 34.5M parameters. PP-OCRv6_medium achieves 86.2% detection Hmean and 83.2% recognition accuracy, outperforming PP-OCRv5_server by +4.6% and +5.1% respectively. + +2. **Lightweight Architectural Innovations:** (i) LCNetV4, a MetaFormer-style lightweight backbone with structural reparameterization; (ii) RepLKFPN, a detection neck with dilated reparameterizable depthwise convolutions; (iii) EncoderWithLightSVTR, a recognition neck with local-global attention and additive skip connections. + +3. **Multi-Language and Scenario Support:** Supports 50 languages and diverse industrial scenes (digital displays, dot-matrix characters, tire prints, etc.), surpassing Qwen3-VL-235B, GPT-5.5, and Gemini-3.1-Pro with orders of magnitude fewer parameters. + +# PP-OCRv6_medium_rec + +## Introduction + +
+ + +PP-OCRv6 text recognition architecture overview +
+ +PP-OCRv6_medium_rec is the largest recognition model in the PP-OCRv6 series. It uses LCNetV4 as the backbone and EncoderWithLightSVTR as the recognition neck, with a CTC+NRTR multi-head decoder. The model supports 50 languages and contains 19M parameters. The key accuracy metrics are as follows: + +| Model | W-Avg | Handwritten CN | Handwritten EN | Printed CN | Printed EN | TC | Ancient | JP | Confusable | Special | General | Pinyin | Artistic | Industrial | Screen | Card | +| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | +| GPT-5.5 | 64.2 | 19.2 | 56.9 | 75.7 | 82.2 | 57.5 | 63.7 | 58.6 | 49.1 | 48.3 | 67.7 | 50.4 | 53.0 | 62.4 | 67.7 | 71.1 | +| Qwen3-VL-235B | 74.9 | 49.7 | 73.2 | 82.3 | 86.2 | 76.4 | 33.6 | 66.2 | 56.1 | 49.0 | 82.5 | 76.5 | 69.6 | 74.7 | 73.8 | 78.7 | +| Kimi-K2.6 | 62.9 | 31.0 | 58.4 | 76.8 | 80.9 | 62.7 | 16.5 | 54.1 | 43.5 | 38.0 | 68.0 | 45.2 | 59.9 | 57.1 | 58.4 | 68.4 | +| MiniMax-M3 | 54.1 | 15.5 | 60.3 | 63.5 | 81.5 | 53.2 | 2.2 | 43.7 | 42.2 | 42.8 | 53.8 | 50.3 | 44.3 | 44.1 | 56.6 | 67.0 | +| Gemini-3.1-Pro | 71.4 | 46.4 | 73.0 | 80.0 | 90.5 | 69.5 | 18.0 | 67.2 | 54.4 | 50.3 | 74.6 | 75.9 | 63.1 | 69.1 | 73.2 | 75.9 | +| PP-OCRv5_server | 78.1 | 58.0 | 59.6 | 90.1 | 85.1 | 74.7 | 60.4 | 73.7 | 59.4 | 56.8 | 86.5 | 74.4 | 64.0 | 70.2 | 68.1 | 87.6 | +| PP-OCRv5_mobile | 73.7 | 41.7 | 50.9 | 86.0 | 86.0 | 72.0 | 57.8 | 75.8 | 55.7 | 54.8 | 80.7 | 72.5 | 54.0 | 59.3 | 57.6 | 81.7 | +| **PP-OCRv6_medium** | **83.2** | **62.1** | **67.8** | **91.5** | **94.1** | **78.6** | **72.4** | **90.5** | **64.9** | **61.7** | **87.5** | **78.1** | **71.2** | **77.4** | **82.5** | **88.1** | +| PP-OCRv6_small | 81.3 | 57.6 | 61.1 | 90.5 | 93.3 | 77.0 | 71.1 | 88.2 | 64.1 | 60.2 | 85.7 | 75.9 | 68.4 | 76.4 | 79.7 | 86.9 | +| PP-OCRv6_tiny | 73.5 | 40.1 | 39.3 | 86.7 | 88.4 | 65.0 | 68.4 | 89.8 | 52.3 | 57.1 | 78.0 | 65.4 | 54.7 | 62.1 | 71.2 | 80.5 | + +## Quick Start + +### Installation + +1. PaddleOCR + +```bash +# Install the basic version +pip install paddleocr + +# Install the full version (includes all features) +pip install "paddleocr[all]" +``` + +2. ONNX Runtime environment (required for onnx models) + +```bash +pip install onnxruntime-gpu +``` + +### Model Usage + +You can quickly experience the functionality with a single command: + +```bash +paddleocr text_recognition \ + --model_name PP-OCRv6_medium_rec \ + --engine onnxruntime \ + -i https://cdn-uploads.huggingface.co/production/uploads/681c1ecd9539bdde5ae1733c/2PZfbirjfxA88695lRmgk.jpeg +``` + +You can also integrate the model inference of the text recognition module into your project. Before running the following code, please download the sample image to your local machine. + +```python +from paddleocr import TextRecognition +model = TextRecognition(model_name="PP-OCRv6_medium_rec", engine="onnxruntime") +output = model.predict(input="2PZfbirjfxA88695lRmgk.jpeg", batch_size=1) +for res in output: + res.print() + res.save_to_json(save_path="./output/res.json") +``` + +After running, the obtained result is as follows: + +```json +{'res': {'input_path': '2PZfbirjfxA88695lRmgk.jpeg', 'page_index': None, 'rec_text': 'day as a reminder of the', 'rec_score': 0.9857}} +``` + +The visualized image is as follows: + +![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/681c1ecd9539bdde5ae1733c/ZDuFDBgLkRcinubSjIvpN.png) + + +For details about usage command and descriptions of parameters, please refer to the [Document](https://paddlepaddle.github.io/PaddleOCR/latest/en/version3.x/module_usage/text_recognition.html). + +### Pipeline Usage + +The general OCR pipeline is used to solve text recognition tasks by extracting text information from images. The pipeline consists of several modules: +* Document Image Orientation Classification Module (Optional) +* Text Image Unwarping Module (Optional) +* Text Line Orientation Classification Module (Optional) +* Text Detection Module +* Text Recognition Module + +Run a single command to quickly experience the OCR pipeline: + +```bash +paddleocr ocr -i https://cdn-uploads.huggingface.co/production/uploads/681c1ecd9539bdde5ae1733c/3ul2Rq4Sk5Cn-l69D695U.png \ + --text_detection_model_name PP-OCRv6_medium_det \ + --text_recognition_model_name PP-OCRv6_medium_rec \ + --engine onnxruntime \ + --use_doc_orientation_classify False \ + --use_doc_unwarping False \ + --use_textline_orientation True \ + --save_path ./output \ + --device gpu:0 +``` + +If save_path is specified, the visualization results will be saved under `save_path`. The visualization output is shown below: + +![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/681c1ecd9539bdde5ae1733c/uhwveSX8KU_wMkyU4jEaw.png) + +For project integration: + +```python +from paddleocr import PaddleOCR + +ocr = PaddleOCR( + text_detection_model_name="PP-OCRv6_medium_det", + text_recognition_model_name="PP-OCRv6_medium_rec", + engine="onnxruntime", + use_doc_orientation_classify=False, + use_doc_unwarping=False, + use_textline_orientation=True, +) +result = ocr.predict("./3ul2Rq4Sk5Cn-l69D695U.png") +for res in result: + res.print() + res.save_to_img("output") + res.save_to_json("output") +``` + + +For details about usage command and descriptions of parameters, please refer to the [Document](https://paddlepaddle.github.io/PaddleOCR/latest/en/version3.x/pipeline_usage/OCR.html#2-quick-start). + +## Links + +[PaddleOCR Repo](https://github.com/paddlepaddle/paddleocr) + +[PaddleOCR Documentation](https://paddlepaddle.github.io/PaddleOCR/latest/en/index.html) + +## Citation + +```bibtex +@misc{zhang2026ppocrv6, + title={PP-OCRv6: From 1.5M to 34.5M Parameters, Surpassing Billion-Scale VLMs on OCR Tasks}, + author={Yubo Zhang and Xueqing Wang and Manhui Lin and Yue Zhang and Penglongyi Deng and Ting Sun and Tingquan Gao and Zelun Zhang and Jiaxuan Liu and Changda Zhou and Hongen Liu and Suyin Liang and Cheng Cui and Yi Liu and Dianhai Yu and Yanjun Ma}, + year={2026}, + eprint={2606.13108}, + archivePrefix={arXiv}, + primaryClass={cs.CV}, + url={https://arxiv.org/abs/2606.13108}, +} +``` diff --git a/models/paddle_ocr/PP-OCRv6_medium_rec_onnx/inference.json b/models/paddle_ocr/PP-OCRv6_medium_rec_onnx/inference.json new file mode 100644 index 0000000..9e01923 --- /dev/null +++ b/models/paddle_ocr/PP-OCRv6_medium_rec_onnx/inference.json @@ -0,0 +1 @@ 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\ No newline at end of file diff --git a/models/paddle_ocr/PP-OCRv6_medium_rec_onnx/inference.onnx b/models/paddle_ocr/PP-OCRv6_medium_rec_onnx/inference.onnx new file mode 100644 index 0000000..55cbb4a Binary files /dev/null and b/models/paddle_ocr/PP-OCRv6_medium_rec_onnx/inference.onnx differ diff --git a/models/paddle_ocr/PP-OCRv6_medium_rec_onnx/inference.yml b/models/paddle_ocr/PP-OCRv6_medium_rec_onnx/inference.yml new file mode 100644 index 0000000..c53a96f --- /dev/null +++ b/models/paddle_ocr/PP-OCRv6_medium_rec_onnx/inference.yml @@ -0,0 +1,18751 @@ +Global: + model_name: PP-OCRv6_medium_rec +Hpi: + backend_configs: + paddle_infer: + trt_dynamic_shapes: &id001 + x: + - - 1 + - 3 + - 48 + - 160 + - - 1 + - 3 + - 48 + - 320 + - - 8 + - 3 + - 48 + - 3200 + tensorrt: + dynamic_shapes: *id001 +PreProcess: + transform_ops: + - DecodeImage: + channel_first: false + img_mode: BGR + - MultiLabelEncode: + gtc_encode: NRTRLabelEncode + - RecResizeImg: + image_shape: + - 3 + - 48 + - 320 + - KeepKeys: + keep_keys: + - image + - label_ctc + - label_gtc + - length + - valid_ratio +PostProcess: + name: CTCLabelDecode + character_dict: + - '!' + - '"' + - '#' + - $ + - '%' + - '&' + - '''' + - ( + - ) + - '*' + - + + - ',' + - '-' + - . + - / + - ':' + - ; + - < + - '=' + - '>' + - '?' + - '@' + - '[' + - \ + - ']' + - ^ + - _ + - '`' + - '{' + - '|' + - '}' + - '~' + - '0' + - '1' + - '2' + - '3' + - '4' + - '5' + - '6' + - '7' + - '8' + - '9' + - A + - B + - C + - D + - E + - F + - G + - H + - I + - J + - K + - L + - M + - N + - O + - P + - Q + - R + - S + - T + - U + - V + - W + - X + - Y + - Z + - a + - b + - c + - d + - e + - f + - g + - h + - i + - j + - k + - l + - m + - n + - o + - p + - q + - r + - s + - t + - u + - v + - w + - x + - y + - z + - ¢ + - £ + - ¤ + - ¥ + - ¦ + - § + - ¨ + - © + - ª + - « + - ¬ + - ® + - ¯ + - ° + - ± + - ² + - ³ + - ´ + - µ + - ¶ + - · + - ¸ + - ¹ + - º + - » + - ¼ + - ½ + - ¾ + - ¿ + - À + - Á + -  + - à + - Ä + - Å + - Æ + - Ç + 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+*.gz filter=lfs diff=lfs merge=lfs -text +*.h5 filter=lfs diff=lfs merge=lfs -text +*.joblib filter=lfs diff=lfs merge=lfs -text +*.lfs.* filter=lfs diff=lfs merge=lfs -text +*.mlmodel filter=lfs diff=lfs merge=lfs -text +*.model filter=lfs diff=lfs merge=lfs -text +*.msgpack filter=lfs diff=lfs merge=lfs -text +*.npy filter=lfs diff=lfs merge=lfs -text +*.npz filter=lfs diff=lfs merge=lfs -text +*.onnx filter=lfs diff=lfs merge=lfs -text +*.ot filter=lfs diff=lfs merge=lfs -text +*.parquet filter=lfs diff=lfs merge=lfs -text +*.pb filter=lfs diff=lfs merge=lfs -text +*.pickle filter=lfs diff=lfs merge=lfs -text +*.pkl filter=lfs diff=lfs merge=lfs -text +*.pt filter=lfs diff=lfs merge=lfs -text +*.pth filter=lfs diff=lfs merge=lfs -text +*.rar filter=lfs diff=lfs merge=lfs -text +*.safetensors filter=lfs diff=lfs merge=lfs -text +saved_model/**/* filter=lfs diff=lfs merge=lfs -text +*.tar.* filter=lfs diff=lfs merge=lfs -text +*.tar filter=lfs diff=lfs merge=lfs -text +*.tflite filter=lfs diff=lfs merge=lfs -text +*.tgz filter=lfs diff=lfs merge=lfs -text +*.wasm filter=lfs diff=lfs merge=lfs -text +*.xz filter=lfs diff=lfs merge=lfs -text +*.zip filter=lfs diff=lfs merge=lfs -text +*.zst filter=lfs diff=lfs merge=lfs -text +*tfevents* filter=lfs diff=lfs merge=lfs -text diff --git a/models/paddle_ocr/PP-OCRv6_small_det_onnx/README.md b/models/paddle_ocr/PP-OCRv6_small_det_onnx/README.md new file mode 100644 index 0000000..6a9d4eb --- /dev/null +++ b/models/paddle_ocr/PP-OCRv6_small_det_onnx/README.md @@ -0,0 +1,181 @@ +--- +license: apache-2.0 +library_name: PaddleOCR +language: +- en +- zh +pipeline_tag: image-to-text +tags: +- OCR +- PaddlePaddle +- PaddleOCR +- textline_detection +- ONNX +--- + +
+ + +

+ +PP-OCRv6: From 1.5M to 34.5M Parameters, Surpassing Billion-Scale VLMs on OCR Tasks + +

+ +[![repo](https://img.shields.io/github/stars/PaddlePaddle/PaddleOCR?color=ccf)](https://github.com/PaddlePaddle/PaddleOCR) 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Model](https://img.shields.io/badge/Paddle_Model-0053CC.svg?logo=data:image/png;base64,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&labelColor=white)](https://huggingface.co/PaddlePaddle/PP-OCRv6_small_det) 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Model](https://img.shields.io/badge/Safetensors_Model-black.svg?logo=data:image/png;base64,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&labelColor=white)](https://huggingface.co/PaddlePaddle/PP-OCRv6_small_det_safetensors) + +**🔥 [Official Website](https://www.paddleocr.com)** +**📝 [Technical Report](https://arxiv.org/pdf/2606.13108)** + +
+ +
+ +
+ +## PP-OCRv6 Overview + +PP-OCRv6 is a lightweight OCR system that combines architectural innovation with data-centric optimization. It redesigns the backbone, detection neck, and recognition neck around a unified MetaFormer-style building block with structural reparameterization. Three model tiers (medium, small, tiny) share the same block primitives, covering deployment scenarios from server to edge. + +### Key Features + +1. **Unified and Scalable Model Family:** A three-tier OCR model family spanning 1.5M to 34.5M parameters. PP-OCRv6_medium achieves 86.2% detection Hmean and 83.2% recognition accuracy, outperforming PP-OCRv5_server by +4.6% and +5.1% respectively. + +2. **Lightweight Architectural Innovations:** (i) LCNetV4, a MetaFormer-style lightweight backbone with structural reparameterization; (ii) RepLKFPN, a detection neck with dilated reparameterizable depthwise convolutions; (iii) EncoderWithLightSVTR, a recognition neck with local-global attention and additive skip connections. + +3. **Multi-Language and Scenario Support:** Supports 48 languages and diverse industrial scenes (digital displays, dot-matrix characters, tire prints, etc.), surpassing Qwen3-VL-235B, GPT-5.5, and Gemini-3.1-Pro with orders of magnitude fewer parameters. + + +# PP-OCRv6_small_det + +## Introduction + +
+ + +PP-OCRv6 text detection architecture overview +
+ +PP-OCRv6_small_det is the small model in the PP-OCRv6 detection series developed by the PaddleOCR team. It uses LCNetV4 as the backbone and RepLKFPN as the feature pyramid neck, providing accurate text localization across diverse scenarios including handwritten, printed, rotated, curved, and artistic text in multiple languages. The model contains 2.48M parameters. The key accuracy metrics are as follows: + +| Model | Average | Handwritten CN | Handwritten EN | Printed CN | Printed EN | Traditional Chinese | Ancient Text | Japanese | Blur | Emoji | Warp | Pinyin | Artistic | Table | Rotation | Industrial | General | +| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | +| Gemini-3.1-Pro | 46.8 | 53.4 | 56.5 | 47.3 | 47.6 | 39.0 | 45.8 | 38.2 | 50.0 | 68.1 | 44.6 | 40.6 | 65.2 | 26.9 | 22.1 | 52.5 | 50.2 | +| GPT-5.5 | 45.6 | 42.4 | 58.5 | 50.2 | 51.9 | 35.0 | 26.7 | 42.0 | 49.1 | 97.5 | 37.7 | 36.3 | 52.0 | 71.0 | 10.0 | 36.2 | 32.6 | +| Qwen3-VL-235B | 38.3 | 56.5 | 66.0 | 41.7 | 37.0 | 19.3 | 13.1 | 27.0 | 38.5 | 81.2 | 28.5 | 33.0 | 68.3 | 19.6 | 2.1 | 48.4 | 32.3 | +| Kimi-K2.6 | 12.8 | 12.5 | 25.5 | 10.1 | 18.5 | 8.2 | 7.5 | 11.2 | 16.9 | 28.9 | 13.9 | 6.8 | 16.1 | 10.9 | 0.8 | 6.3 | 10.9 | +| MiniMax-M3 | 12.0 | 13.7 | 19.3 | 9.8 | 14.1 | 7.7 | 11.1 | 10.6 | 16.1 | 32.8 | 12.8 | 8.5 | 16.6 | 5.5 | 0.1 | 6.4 | 6.4 | +| PP-OCRv5_server | 81.6 | 80.3 | 84.1 | 94.5 | 91.7 | 81.5 | 67.6 | 77.2 | 90.1 | 96.2 | 87.6 | 67.1 | 67.3 | 97.1 | 80.0 | 64.3 | 79.7 | +| PP-OCRv5_mobile | 75.2 | 74.4 | 77.7 | 90.5 | 91.0 | 82.3 | 58.1 | 72.7 | 87.4 | 93.6 | 82.7 | 57.5 | 52.5 | 92.8 | 64.7 | 52.8 | 72.1 | +| PP-OCRv6_medium | 86.2 | 83.7 | 84.0 | 95.1 | 93.7 | 86.3 | 80.2 | 84.3 | 94.1 | 99.6 | 88.6 | 74.0 | 69.0 | 96.8 | 93.8 | 73.3 | 82.8 | +| **PP-OCRv6_small** | **84.1** | **80.5** | **87.1** | **94.2** | **93.6** | **85.7** | **72.6** | **82.3** | **92.6** | **99.7** | **87.6** | **69.6** | **65.3** | **95.6** | **93.7** | **67.6** | **78.2** | +| PP-OCRv6_tiny | 80.6 | 79.4 | 85.9 | 93.1 | 92.3 | 83.7 | 63.0 | 76.6 | 89.3 | 99.8 | 86.1 | 59.0 | 60.1 | 94.7 | 91.0 | 62.0 | 73.8 | + +## Quick Start + +### Installation + +```bash +# Install PaddleOCR +pip install paddleocr + +# Install ONNX Runtime +pip install onnxruntime-gpu # or onnxruntime for CPU-only +``` + +### Model Usage + +You can quickly experience the functionality with a single command: + +```bash +paddleocr text_detection \ + --model_name PP-OCRv6_small_det \ + --engine onnxruntime \ + -i https://cdn-uploads.huggingface.co/production/uploads/681c1ecd9539bdde5ae1733c/3ul2Rq4Sk5Cn-l69D695U.png +``` + +You can also integrate the model inference of the text detection module into your project. Before running the following code, please download the sample image to your local machine. + +```python +from paddleocr import TextDetection +model = TextDetection(model_name="PP-OCRv6_small_det", engine="onnxruntime") +output = model.predict(input="3ul2Rq4Sk5Cn-l69D695U.png", batch_size=1) +for res in output: + res.print() + res.save_to_img(save_path="./output/") + res.save_to_json(save_path="./output/res.json") +``` + + +For details about usage command and descriptions of parameters, please refer to the [Document](https://paddlepaddle.github.io/PaddleOCR/latest/en/version3.x/module_usage/text_detection.html#iii-quick-start). + +### Pipeline Usage + +The general OCR pipeline extracts text information from images. The pipeline consists of several modules: +* Document Image Orientation Classification Module (Optional) +* Text Image Unwarping Module (Optional) +* Text Line Orientation Classification Module (Optional) +* Text Detection Module +* Text Recognition Module + +Run a single command to quickly experience the OCR pipeline: + +```bash +paddleocr ocr -i https://cdn-uploads.huggingface.co/production/uploads/681c1ecd9539bdde5ae1733c/3ul2Rq4Sk5Cn-l69D695U.png \ + --text_detection_model_name PP-OCRv6_small_det \ + --text_recognition_model_name PP-OCRv6_small_rec \ + --engine onnxruntime \ + --use_doc_orientation_classify False \ + --use_doc_unwarping False \ + --use_textline_orientation True \ + --save_path ./output \ + --device gpu:0 +``` + +For project integration: + +```python +from paddleocr import PaddleOCR + +ocr = PaddleOCR( + text_detection_model_name="PP-OCRv6_small_det", + text_recognition_model_name="PP-OCRv6_small_rec", + engine="onnxruntime", + use_doc_orientation_classify=False, + use_doc_unwarping=False, + use_textline_orientation=False, +) +result = ocr.predict("./3ul2Rq4Sk5Cn-l69D695U.png") +for res in result: + res.print() + res.save_to_img("output") + res.save_to_json("output") +``` + + +For details about usage command and descriptions of parameters, please refer to the [Document](https://paddlepaddle.github.io/PaddleOCR/latest/en/version3.x/pipeline_usage/OCR.html#2-quick-start). + +## Links + +[PaddleOCR Repo](https://github.com/paddlepaddle/paddleocr) + +[PaddleOCR Documentation](https://paddlepaddle.github.io/PaddleOCR/latest/en/index.html) + +## Citation + +```bibtex +@misc{zhang2026ppocrv6, + title={PP-OCRv6: From 1.5M to 34.5M Parameters, Surpassing Billion-Scale VLMs on OCR Tasks}, + author={Yubo Zhang and Xueqing Wang and Manhui Lin and Yue Zhang and Penglongyi Deng and Ting Sun and Tingquan Gao and Zelun Zhang and Jiaxuan Liu and Changda Zhou and Hongen Liu and Suyin Liang and Cheng Cui and Yi Liu and Dianhai Yu and Yanjun Ma}, + year={2026}, + eprint={2606.13108}, + archivePrefix={arXiv}, + primaryClass={cs.CV}, + url={https://arxiv.org/abs/2606.13108}, +} +``` diff --git a/models/paddle_ocr/PP-OCRv6_small_det_onnx/inference.json b/models/paddle_ocr/PP-OCRv6_small_det_onnx/inference.json new file mode 100644 index 0000000..c380578 --- /dev/null +++ b/models/paddle_ocr/PP-OCRv6_small_det_onnx/inference.json @@ -0,0 +1 @@ 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\ No newline at end of file diff --git a/models/paddle_ocr/PP-OCRv6_small_det_onnx/inference.onnx b/models/paddle_ocr/PP-OCRv6_small_det_onnx/inference.onnx new file mode 100644 index 0000000..15b8ac4 Binary files /dev/null and b/models/paddle_ocr/PP-OCRv6_small_det_onnx/inference.onnx differ diff --git a/models/paddle_ocr/PP-OCRv6_small_det_onnx/inference.yml b/models/paddle_ocr/PP-OCRv6_small_det_onnx/inference.yml new file mode 100644 index 0000000..9ce7e05 --- /dev/null +++ b/models/paddle_ocr/PP-OCRv6_small_det_onnx/inference.yml @@ -0,0 +1,52 @@ +Global: + model_name: PP-OCRv6_small_det +Hpi: + backend_configs: + paddle_infer: + trt_dynamic_shapes: &id001 + x: + - - 1 + - 3 + - 32 + - 32 + - - 1 + - 3 + - 736 + - 736 + - - 1 + - 3 + - 4000 + - 4000 + tensorrt: + dynamic_shapes: *id001 +PostProcess: + box_thresh: 0.45 + max_candidates: 3000 + name: DBPostProcess + thresh: 0.2 + unclip_ratio: 1.4 +PreProcess: + transform_ops: + - DecodeImage: + channel_first: false + img_mode: BGR + - DetLabelEncode: null + - DetResizeForTest: null + - NormalizeImage: + mean: + - 0.485 + - 0.456 + - 0.406 + order: hwc + scale: 1./255. + std: + - 0.229 + - 0.224 + - 0.225 + - ToCHWImage: null + - KeepKeys: + keep_keys: + - image + - shape + - polys + - ignore_tags diff --git a/models/paddle_ocr/PP-OCRv6_small_rec_onnx/.gitattributes b/models/paddle_ocr/PP-OCRv6_small_rec_onnx/.gitattributes new file mode 100644 index 0000000..a6344aa --- /dev/null +++ b/models/paddle_ocr/PP-OCRv6_small_rec_onnx/.gitattributes @@ -0,0 +1,35 @@ +*.7z filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text +*.bin filter=lfs diff=lfs merge=lfs -text +*.bz2 filter=lfs diff=lfs merge=lfs -text +*.ckpt filter=lfs diff=lfs merge=lfs -text +*.ftz filter=lfs diff=lfs merge=lfs -text +*.gz filter=lfs diff=lfs merge=lfs -text +*.h5 filter=lfs diff=lfs merge=lfs -text +*.joblib filter=lfs diff=lfs merge=lfs -text +*.lfs.* filter=lfs diff=lfs merge=lfs -text +*.mlmodel filter=lfs diff=lfs merge=lfs -text +*.model filter=lfs diff=lfs merge=lfs -text +*.msgpack filter=lfs diff=lfs merge=lfs -text +*.npy filter=lfs diff=lfs merge=lfs -text +*.npz filter=lfs diff=lfs merge=lfs -text +*.onnx filter=lfs diff=lfs merge=lfs -text +*.ot filter=lfs diff=lfs merge=lfs -text +*.parquet filter=lfs diff=lfs merge=lfs -text +*.pb filter=lfs diff=lfs merge=lfs -text +*.pickle filter=lfs diff=lfs merge=lfs -text +*.pkl filter=lfs diff=lfs merge=lfs -text +*.pt filter=lfs diff=lfs merge=lfs -text +*.pth filter=lfs diff=lfs merge=lfs -text +*.rar filter=lfs diff=lfs merge=lfs -text +*.safetensors filter=lfs diff=lfs merge=lfs -text +saved_model/**/* filter=lfs diff=lfs merge=lfs -text +*.tar.* filter=lfs diff=lfs merge=lfs -text +*.tar filter=lfs diff=lfs merge=lfs -text +*.tflite filter=lfs diff=lfs merge=lfs -text +*.tgz filter=lfs diff=lfs merge=lfs -text +*.wasm filter=lfs diff=lfs merge=lfs -text +*.xz filter=lfs diff=lfs merge=lfs -text +*.zip filter=lfs diff=lfs merge=lfs -text +*.zst filter=lfs diff=lfs merge=lfs -text +*tfevents* filter=lfs diff=lfs merge=lfs -text diff --git a/models/paddle_ocr/PP-OCRv6_small_rec_onnx/README.md b/models/paddle_ocr/PP-OCRv6_small_rec_onnx/README.md new file mode 100644 index 0000000..1a37eb5 --- /dev/null +++ b/models/paddle_ocr/PP-OCRv6_small_rec_onnx/README.md @@ -0,0 +1,200 @@ +--- +license: apache-2.0 +library_name: PaddleOCR +language: +- en +- zh +pipeline_tag: image-to-text +tags: +- OCR +- PaddlePaddle +- PaddleOCR +- text_recognition +--- + +
+ + +

+ +PP-OCRv6: From 1.5M to 34.5M Parameters, Surpassing Billion-Scale VLMs on OCR Tasks + +

+ +[![repo](https://img.shields.io/github/stars/PaddlePaddle/PaddleOCR?color=ccf)](https://github.com/PaddlePaddle/PaddleOCR) +[![HuggingFace](https://img.shields.io/badge/HuggingFace-black.svg?logo=data:image/png;base64,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&labelColor=white)](https://huggingface.co/PaddlePaddle/PP-OCRv6_small_rec_onnx) +[![X](https://img.shields.io/badge/X-PaddlePaddle-6080F0)](https://x.com/PaddlePaddle) +[![License](https://img.shields.io/badge/license-Apache_2.0-green)](./LICENSE) +[![Paddle Model](https://img.shields.io/badge/Paddle_Model-0053CC.svg?logo=data:image/png;base64,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&labelColor=white)](https://huggingface.co/PaddlePaddle/PP-OCRv6_small_rec) +[![Safetensors Model](https://img.shields.io/badge/Safetensors_Model-black.svg?logo=data:image/png;base64,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&labelColor=white)](https://huggingface.co/PaddlePaddle/PP-OCRv6_small_rec_safetensors) + +**🔥 [Official Website](https://www.paddleocr.com)** +**📝 [Technical Report](https://arxiv.org/pdf/2606.13108)** + +
+ +
+ +
+ +## PP-OCRv6 Overview + +PP-OCRv6 is a lightweight OCR system that combines architectural innovation with data-centric optimization. It redesigns the backbone, detection neck, and recognition neck around a unified MetaFormer-style building block with structural reparameterization. Three model tiers (medium, small, tiny) share the same block primitives, covering deployment scenarios from server to edge. + +### Key Features + +1. **Unified and Scalable Model Family:** A three-tier OCR model family spanning 1.5M to 34.5M parameters. PP-OCRv6_medium achieves 86.2% detection Hmean and 83.2% recognition accuracy, outperforming PP-OCRv5_server by +4.6% and +5.1% respectively. + +2. **Lightweight Architectural Innovations:** (i) LCNetV4, a MetaFormer-style lightweight backbone with structural reparameterization; (ii) RepLKFPN, a detection neck with dilated reparameterizable depthwise convolutions; (iii) EncoderWithLightSVTR, a recognition neck with local-global attention and additive skip connections. + +3. **Multi-Language and Scenario Support:** Supports 50 languages and diverse industrial scenes (digital displays, dot-matrix characters, tire prints, etc.), surpassing Qwen3-VL-235B, GPT-5.5, and Gemini-3.1-Pro with orders of magnitude fewer parameters. + +# PP-OCRv6_medium_rec + +## Introduction + +
+ + +PP-OCRv6 text recognition architecture overview +
+ +PP-OCRv6_small_rec is the mid-tier recognition model in the PP-OCRv6 series. It uses LCNetV4 as the backbone and EncoderWithLightSVTR as the recognition neck, with a CTC+NRTR multi-head decoder. The model supports 50 languages and contains 5.2M parameters. The key accuracy metrics are as follows: + +| Model | W-Avg | Handwritten CN | Handwritten EN | Printed CN | Printed EN | TC | Ancient | JP | Confusable | Special | General | Pinyin | Artistic | Industrial | Screen | Card | +| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | +| GPT-5.5 | 64.2 | 19.2 | 56.9 | 75.7 | 82.2 | 57.5 | 63.7 | 58.6 | 49.1 | 48.3 | 67.7 | 50.4 | 53.0 | 62.4 | 67.7 | 71.1 | +| Qwen3-VL-235B | 74.9 | 49.7 | 73.2 | 82.3 | 86.2 | 76.4 | 33.6 | 66.2 | 56.1 | 49.0 | 82.5 | 76.5 | 69.6 | 74.7 | 73.8 | 78.7 | +| Kimi-K2.6 | 62.9 | 31.0 | 58.4 | 76.8 | 80.9 | 62.7 | 16.5 | 54.1 | 43.5 | 38.0 | 68.0 | 45.2 | 59.9 | 57.1 | 58.4 | 68.4 | +| MiniMax-M3 | 54.1 | 15.5 | 60.3 | 63.5 | 81.5 | 53.2 | 2.2 | 43.7 | 42.2 | 42.8 | 53.8 | 50.3 | 44.3 | 44.1 | 56.6 | 67.0 | +| Gemini-3.1-Pro | 71.4 | 46.4 | 73.0 | 80.0 | 90.5 | 69.5 | 18.0 | 67.2 | 54.4 | 50.3 | 74.6 | 75.9 | 63.1 | 69.1 | 73.2 | 75.9 | +| PP-OCRv5_server | 78.1 | 58.0 | 59.6 | 90.1 | 85.1 | 74.7 | 60.4 | 73.7 | 59.4 | 56.8 | 86.5 | 74.4 | 64.0 | 70.2 | 68.1 | 87.6 | +| PP-OCRv5_mobile | 73.7 | 41.7 | 50.9 | 86.0 | 86.0 | 72.0 | 57.8 | 75.8 | 55.7 | 54.8 | 80.7 | 72.5 | 54.0 | 59.3 | 57.6 | 81.7 | +| PP-OCRv6_medium | 83.2 | 62.1 | 67.8 | 91.5 | 94.1 | 78.6 | 72.4 | 90.5 | 64.9 | 61.7 | 87.5 | 78.1 | 71.2 | 77.4 | 82.5 | 88.1 | +| **PP-OCRv6_small** | **81.3** | **57.6** | **61.1** | **90.5** | **93.3** | **77.0** | **71.1** | **88.2** | **64.1** | **60.2** | **85.7** | **75.9** | **68.4** | **76.4** | **79.7** | **86.9** | +| PP-OCRv6_tiny | 73.5 | 40.1 | 39.3 | 86.7 | 88.4 | 65.0 | 68.4 | 89.8 | 52.3 | 57.1 | 78.0 | 65.4 | 54.7 | 62.1 | 71.2 | 80.5 | + +## Quick Start + +### Installation + +1. PaddleOCR + +```bash +# Install the basic version +pip install paddleocr + +# Install the full version (includes all features) +pip install "paddleocr[all]" +``` + +2. ONNX Runtime environment (required for onnx models) + +```bash +pip install onnxruntime-gpu +``` + +### Model Usage + +You can quickly experience the functionality with a single command: + +```bash +paddleocr text_recognition \ + --model_name PP-OCRv6_medium_rec \ + --engine onnxruntime \ + -i https://cdn-uploads.huggingface.co/production/uploads/681c1ecd9539bdde5ae1733c/2PZfbirjfxA88695lRmgk.jpeg +``` + +You can also integrate the model inference of the text recognition module into your project. Before running the following code, please download the sample image to your local machine. + +```python +from paddleocr import TextRecognition +model = TextRecognition(model_name="PP-OCRv6_medium_rec", engine="onnxruntime") +output = model.predict(input="2PZfbirjfxA88695lRmgk.jpeg", batch_size=1) +for res in output: + res.print() + res.save_to_json(save_path="./output/res.json") +``` + +After running, the obtained result is as follows: + +```json +{'res': {'input_path': '2PZfbirjfxA88695lRmgk.jpeg', 'page_index': None, 'rec_text': 'day as a reminder of the', 'rec_score': 0.9857}} +``` + +The visualized image is as follows: + +![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/681c1ecd9539bdde5ae1733c/ZDuFDBgLkRcinubSjIvpN.png) + + +For details about usage command and descriptions of parameters, please refer to the [Document](https://paddlepaddle.github.io/PaddleOCR/latest/en/version3.x/module_usage/text_recognition.html). + +### Pipeline Usage + +The general OCR pipeline is used to solve text recognition tasks by extracting text information from images. The pipeline consists of several modules: +* Document Image Orientation Classification Module (Optional) +* Text Image Unwarping Module (Optional) +* Text Line Orientation Classification Module (Optional) +* Text Detection Module +* Text Recognition Module + +Run a single command to quickly experience the OCR pipeline: + +```bash +paddleocr ocr -i https://cdn-uploads.huggingface.co/production/uploads/681c1ecd9539bdde5ae1733c/3ul2Rq4Sk5Cn-l69D695U.png \ + --text_detection_model_name PP-OCRv6_medium_det \ + --text_recognition_model_name PP-OCRv6_medium_rec \ + --engine onnxruntime \ + --use_doc_orientation_classify False \ + --use_doc_unwarping False \ + --use_textline_orientation True \ + --save_path ./output \ + --device gpu:0 +``` + +If save_path is specified, the visualization results will be saved under `save_path`. The visualization output is shown below: + +![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/681c1ecd9539bdde5ae1733c/uhwveSX8KU_wMkyU4jEaw.png) + +For project integration: + +```python +from paddleocr import PaddleOCR + +ocr = PaddleOCR( + text_detection_model_name="PP-OCRv6_medium_det", + text_recognition_model_name="PP-OCRv6_medium_rec", + engine="onnxruntime", + use_doc_orientation_classify=False, + use_doc_unwarping=False, + use_textline_orientation=True, +) +result = ocr.predict("./3ul2Rq4Sk5Cn-l69D695U.png") +for res in result: + res.print() + res.save_to_img("output") + res.save_to_json("output") +``` + + +For details about usage command and descriptions of parameters, please refer to the [Document](https://paddlepaddle.github.io/PaddleOCR/latest/en/version3.x/pipeline_usage/OCR.html#2-quick-start). + +## Links + +[PaddleOCR Repo](https://github.com/paddlepaddle/paddleocr) + +[PaddleOCR Documentation](https://paddlepaddle.github.io/PaddleOCR/latest/en/index.html) + +## Citation + +```bibtex +@misc{zhang2026ppocrv6, + title={PP-OCRv6: From 1.5M to 34.5M Parameters, Surpassing Billion-Scale VLMs on OCR Tasks}, + author={Yubo Zhang and Xueqing Wang and Manhui Lin and Yue Zhang and Penglongyi Deng and Ting Sun and Tingquan Gao and Zelun Zhang and Jiaxuan Liu and Changda Zhou and Hongen Liu and Suyin Liang and Cheng Cui and Yi Liu and Dianhai Yu and Yanjun Ma}, + year={2026}, + eprint={2606.13108}, + archivePrefix={arXiv}, + primaryClass={cs.CV}, + url={https://arxiv.org/abs/2606.13108}, +} +``` diff --git a/models/paddle_ocr/PP-OCRv6_small_rec_onnx/inference.json b/models/paddle_ocr/PP-OCRv6_small_rec_onnx/inference.json new file mode 100644 index 0000000..8926702 --- /dev/null +++ b/models/paddle_ocr/PP-OCRv6_small_rec_onnx/inference.json @@ -0,0 +1 @@ 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\ No newline at end of file diff --git a/models/paddle_ocr/PP-OCRv6_small_rec_onnx/inference.onnx b/models/paddle_ocr/PP-OCRv6_small_rec_onnx/inference.onnx new file mode 100644 index 0000000..7c05294 Binary files /dev/null and b/models/paddle_ocr/PP-OCRv6_small_rec_onnx/inference.onnx differ diff --git a/models/paddle_ocr/PP-OCRv6_small_rec_onnx/inference.yml b/models/paddle_ocr/PP-OCRv6_small_rec_onnx/inference.yml new file mode 100644 index 0000000..f188759 --- /dev/null +++ b/models/paddle_ocr/PP-OCRv6_small_rec_onnx/inference.yml @@ -0,0 +1,18751 @@ +Global: + model_name: PP-OCRv6_small_rec +Hpi: + backend_configs: + paddle_infer: + trt_dynamic_shapes: &id001 + x: + - - 1 + - 3 + - 48 + - 160 + - - 1 + - 3 + - 48 + - 320 + - - 8 + - 3 + - 48 + - 3200 + tensorrt: + dynamic_shapes: *id001 +PreProcess: + transform_ops: + - DecodeImage: + channel_first: false + img_mode: BGR + - MultiLabelEncode: + gtc_encode: NRTRLabelEncode + - RecResizeImg: + image_shape: + - 3 + - 48 + - 320 + - 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+van +vanish +vapor +various +vast +vault +vehicle +velvet +vendor +venture +venue +verb +verify +version +very +vessel +veteran +viable +vibrant +vicious +victory +video +view +village +vintage +violin +virtual +virus +visa +visit +visual +vital +vivid +vocal +voice +void +volcano +volume +vote +voyage +wage +wagon +wait +walk +wall +walnut +want +warfare +warm +warrior +wash +wasp +waste +water +wave +way +wealth +weapon +wear +weasel +weather +web +wedding +weekend +weird +welcome +west +wet +whale +what +wheat +wheel +when +where +whip +whisper +wide +width +wife +wild +will +win +window +wine +wing +wink +winner +winter +wire +wisdom +wise +wish +witness +wolf +woman +wonder +wood +wool +word +work +world +worry +worth +wrap +wreck +wrestle +wrist +write +wrong +yard +year +yellow +you +young +youth +zebra +zero +zone +zoo diff --git a/scripts/paddleocr_en_infer.py b/scripts/paddleocr_en_infer.py index 9c3b33d..bf19ec9 100644 --- a/scripts/paddleocr_en_infer.py +++ b/scripts/paddleocr_en_infer.py @@ -1,12 +1,13 @@ #!/usr/bin/env python3 """ -Run en_PP-OCRv5_mobile_rec (local Paddle inference) on a single image. +Run PP-OCRv6 ONNX detection/recognition on a single image. Input JSON (stdin line): { "imageDataUrl": "data:image/jpeg;base64,...", "layoutHint": "mnemonic" | "verify-options" | "verify-number" | "generic", # optional - "expectedWordCount": 12 | 18 | 20 | 24 # optional + "expectedWordCount": 12 | 18 | 20 | 24 | 33, # optional + "wordlistHint": "bip39" | "slip39" # optional, which closed wordlist the flow uses } Output JSON (stdout line): @@ -45,16 +46,29 @@ from PIL import Image import yaml -from paddle import inference -from paddleocr import PaddleOCR +from paddleocr import PaddleOCR, TextRecognition -EN_REC_MODEL_DIR_NAME = "en_PP-OCRv5_mobile_rec" -MULTI_REC_MODEL_DIR_NAME = "PP-OCRv5_mobile_rec" -DET_MODEL_DIR_NAME = "PP-OCRv5_mobile_det" +REC_MODEL_DIR_NAME = "PP-OCRv6_medium_rec_onnx" +DET_MODEL_DIR_NAME = "PP-OCRv6_medium_det_onnx" +DEFAULT_REC_MODEL_NAME = "PP-OCRv6_medium_rec" +DEFAULT_DET_MODEL_NAME = "PP-OCRv6_medium_det" +OCR_ENGINE = "onnxruntime" + +# Bump when tuning recognition; the Electron main process logs it at startup so +# a stale (pre-restart) OCR server is immediately visible. +SCRIPT_VERSION = "2026-07-03.4-blue-channel" MODEL_CACHE: Dict[str, Tuple[Any, Any, Any, List[str]]] = {} VERIFY_NUMBER_OCR: Optional[PaddleOCR] = None +# Closed wordlists for mnemonic recognition. BIP39 (2048 words) and SLIP39 +# (1024 words) are distinct lists — never mix them; the renderer says which +# one the current flow uses via the request's wordlistHint. +Lexicon = Tuple[frozenset, Dict[int, List[str]]] +LEXICON_FILES = {"bip39": "bip39_english.txt", "slip39": "slip39_english.txt"} +LEXICON_MIN_WORDS = {"bip39": 2048, "slip39": 1024} +LEXICON_CACHE: Dict[str, Optional[Lexicon]] = {} + def read_int_env(name: str, default: int, minimum: int, maximum: int) -> int: raw = os.environ.get(name) @@ -123,15 +137,19 @@ def resolve_model_dir(default_name: str, env_keys: Sequence[str]) -> Path: def resolve_en_model_dir() -> Path: return resolve_model_dir( - default_name=EN_REC_MODEL_DIR_NAME, + default_name=REC_MODEL_DIR_NAME, env_keys=["QA_AUTO_HW_OCR_MODEL_DIR", "QA_AUTO_HW_EN_OCR_MODEL_DIR"], ) def resolve_multi_rec_model_dir() -> Path: return resolve_model_dir( - default_name=MULTI_REC_MODEL_DIR_NAME, - env_keys=["QA_AUTO_HW_OCR_MULTI_REC_MODEL_DIR"], + default_name=REC_MODEL_DIR_NAME, + env_keys=[ + "QA_AUTO_HW_OCR_MULTI_REC_MODEL_DIR", + "QA_AUTO_HW_OCR_MODEL_DIR", + "QA_AUTO_HW_EN_OCR_MODEL_DIR", + ], ) @@ -174,31 +192,50 @@ def load_charset(model_dir: Path) -> List[str]: return [str(x) for x in chars] +def load_model_name(model_dir: Path, default_name: str) -> str: + yml_path = model_dir / "inference.yml" + try: + data = yaml.safe_load(yml_path.read_text(encoding="utf-8")) + model_name = data.get("Global", {}).get("model_name") + return str(model_name) if model_name else default_name + except Exception: + return default_name + + +class OnnxTextRecognizer: + def __init__(self, model_dir: Path): + model_file = model_dir / "inference.onnx" + if not model_file.exists(): + raise FileNotFoundError( + f"Model files missing in {model_dir}. Expected {model_file.name}." + ) + self.model = TextRecognition( + model_name=load_model_name(model_dir, DEFAULT_REC_MODEL_NAME), + model_dir=str(model_dir), + engine=OCR_ENGINE, + ) + + def recognize(self, image_bgr: np.ndarray) -> Tuple[str, float]: + result = self.model.predict(image_bgr, batch_size=1) + for item in result: + if isinstance(item, dict): + text = item.get("rec_text") + score = item.get("rec_score") + if isinstance(text, str): + conf = float(score) if isinstance(score, (int, float, np.floating)) else 0.0 + return text, conf + return "", 0.0 + + def ensure_rec_model(model_dir: Path) -> Tuple[Any, Any, Any, List[str]]: model_key = str(model_dir.resolve()) cached = MODEL_CACHE.get(model_key) if cached is not None: return cached - model_file = model_dir / "inference.json" - params_file = model_dir / "inference.pdiparams" - if not model_file.exists() or not params_file.exists(): - raise FileNotFoundError( - f"Model files missing in {model_dir}. " - f"Expected {model_file.name} and {params_file.name}." - ) - - config = inference.Config(str(model_file), str(params_file)) - config.disable_gpu() - config.set_cpu_math_library_num_threads(CPU_THREADS) - config.disable_mkldnn() - config.switch_use_feed_fetch_ops(False) - # Keep behavior stable across machines. - config.switch_ir_optim(False) - - predictor = inference.create_predictor(config) - input_handle = predictor.get_input_handle(predictor.get_input_names()[0]) - output_handle = predictor.get_output_handle(predictor.get_output_names()[0]) + predictor = OnnxTextRecognizer(model_dir) + input_handle = None + output_handle = None charset = load_charset(model_dir) cached = (predictor, input_handle, output_handle, charset) MODEL_CACHE[model_key] = cached @@ -211,25 +248,25 @@ def ensure_verify_number_ocr() -> PaddleOCR: return VERIFY_NUMBER_OCR det_dir = resolve_det_model_dir() - multi_rec_dir = resolve_multi_rec_model_dir() + rec_dir = resolve_multi_rec_model_dir() init_params = inspect.signature(PaddleOCR.__init__).parameters - kwargs: Dict[str, Any] = {} + kwargs: Dict[str, Any] = {"engine": OCR_ENGINE} # PaddleOCR 3.x naming. if "text_detection_model_dir" in init_params: kwargs["text_detection_model_dir"] = str(det_dir) if "text_detection_model_name" in init_params: - kwargs["text_detection_model_name"] = "PP-OCRv5_mobile_det" + kwargs["text_detection_model_name"] = load_model_name(det_dir, DEFAULT_DET_MODEL_NAME) else: kwargs["det_model_dir"] = str(det_dir) if "text_recognition_model_dir" in init_params: - kwargs["text_recognition_model_dir"] = str(multi_rec_dir) + kwargs["text_recognition_model_dir"] = str(rec_dir) if "text_recognition_model_name" in init_params: - kwargs["text_recognition_model_name"] = "PP-OCRv5_mobile_rec" + kwargs["text_recognition_model_name"] = load_model_name(rec_dir, DEFAULT_REC_MODEL_NAME) else: - kwargs["rec_model_dir"] = str(multi_rec_dir) + kwargs["rec_model_dir"] = str(rec_dir) # Keep current behavior: detect + recognize only. if "use_doc_orientation_classify" in init_params: @@ -293,7 +330,80 @@ def build_rec_crop_variants(crop_bgr: np.ndarray, variant_count: int = REC_VARIA return variants -def score_rec_text(text: str, conf: float) -> float: +def load_lexicon(name: str) -> Optional[Lexicon]: + """Loads a wordlist (scripts/{bip39,slip39}_english.txt) once, keyed by name. + + Returns (word_set, words_by_length) or None when the file is unavailable. + """ + if name in LEXICON_CACHE: + return LEXICON_CACHE[name] + + file_name = LEXICON_FILES.get(name) + if file_name is None: + LEXICON_CACHE[name] = None + return None + + wordlist_path = Path(__file__).resolve().parent / file_name + lexicon: Optional[Lexicon] = None + try: + words = [line.strip().lower() for line in wordlist_path.read_text(encoding="utf-8").splitlines()] + words = [w for w in words if w] + if len(words) >= LEXICON_MIN_WORDS[name]: + by_length: Dict[int, List[str]] = {} + for word in words: + by_length.setdefault(len(word), []).append(word) + lexicon = (frozenset(words), by_length) + except OSError: + lexicon = None + + LEXICON_CACHE[name] = lexicon + return lexicon + + +def within_one_edit(a: str, b: str) -> bool: + la, lb = len(a), len(b) + if abs(la - lb) > 1: + return False + if la == lb: + diff = 0 + for ca, cb in zip(a, b): + if ca != cb: + diff += 1 + if diff > 1: + return False + return True + if la > lb: + a, b, la, lb = b, a, lb, la + i = j = 0 + skipped = False + while i < la and j < lb: + if a[i] == b[j]: + i += 1 + j += 1 + elif skipped: + return False + else: + skipped = True + j += 1 + return True + + +def lexicon_has_near_match( + lexicon: Lexicon, token: str +) -> bool: + _, by_length = lexicon + for length in (len(token) - 1, len(token), len(token) + 1): + for word in by_length.get(length, ()): + if within_one_edit(token, word): + return True + return False + + +def score_rec_text( + text: str, + conf: float, + lexicon: Optional[Lexicon] = None, +) -> float: token = extract_alpha_token(text) score = conf if 3 <= len(token) <= 8: @@ -306,10 +416,64 @@ def score_rec_text(text: str, conf: float) -> float: alpha_tokens = re.findall(r"[A-Za-z]+", text) if len(alpha_tokens) > 1: score -= 0.04 * (len(alpha_tokens) - 1) + + # Closed-vocabulary bonus: mnemonic/verify words always come from the selected + # wordlist, so prefer the variant whose decode lands in (or near) that list. + if lexicon is not None and token: + words, _ = lexicon + if token in words: + score += 0.25 + elif lexicon_has_near_match(lexicon, token): + score += 0.08 + else: + score -= 0.12 return score -def decode_ctc(logits: np.ndarray, charset: Sequence[str]) -> Tuple[str, float]: +# Per-scene charset masks. Restricting the CTC decode to characters that can +# actually appear kills digit/letter confusions (0/O, 1/l) at the source: +# - "letters": mnemonic words / verify options. Pure letters on both devices +# (pro is all-lowercase, pro2 capitalizes the first letter on verify pages), +# so [A-Za-z] covers both; the index digits come from row order, not OCR. +# - "alnum-index": index/number regions and generic text ("1." style indices up +# to 33, "#N" markers) — letters, digits and the few separators that occur. +CHARSET_MASK_PATTERNS = { + "letters": re.compile(r"[A-Za-z ]"), + "alnum-index": re.compile(r"[A-Za-z0-9.# ]"), +} +CHARSET_MASK_CACHE: Dict[Tuple[str, int], Optional[np.ndarray]] = {} + + +def build_charset_mask(charset: Sequence[str], mask_name: str) -> Optional[np.ndarray]: + """Boolean mask over CTC classes (blank + charset) allowing only mask_name chars. + + Charset lists are cached per model in MODEL_CACHE, so id() is a stable key. + """ + pattern = CHARSET_MASK_PATTERNS.get(mask_name) + if pattern is None: + return None + cache_key = (mask_name, id(charset)) + cached = CHARSET_MASK_CACHE.get(cache_key) + if cached is not None: + return cached + + mask = np.zeros(len(charset) + 1, dtype=bool) + mask[0] = True # CTC blank must stay decodable + for pos, char in enumerate(charset): + if pattern.fullmatch(char): + mask[pos + 1] = True + CHARSET_MASK_CACHE[cache_key] = mask + return mask + + +def decode_ctc( + logits: np.ndarray, + charset: Sequence[str], + allowed_mask: Optional[np.ndarray] = None, +) -> Tuple[str, float]: + if allowed_mask is not None and allowed_mask.shape[0] == logits.shape[1]: + # Probabilities are >= 0, so -1 reliably excludes disallowed classes. + logits = np.where(allowed_mask[None, :], logits, -1.0) idxs = logits.argmax(axis=1) confs = logits.max(axis=1) chars: List[str] = [] @@ -345,6 +509,8 @@ def recognize_crop( output_handle: Any, charset: Sequence[str], variant_count: int = REC_VARIANT_COUNT, + lexicon: Optional[Lexicon] = None, + charset_mask: Optional[np.ndarray] = None, ) -> Tuple[str, float]: x, y, w, h = box h_img, w_img = image_bgr.shape[:2] @@ -358,13 +524,16 @@ def recognize_crop( best_score = float("-inf") for variant in build_rec_crop_variants(crop, variant_count): - arr = preprocess_rec_input(variant) - input_handle.reshape(arr.shape) - input_handle.copy_from_cpu(arr) - predictor.run() - out = output_handle.copy_to_cpu()[0] - text, conf = decode_ctc(out, charset) - score = score_rec_text(text, conf) + if isinstance(predictor, OnnxTextRecognizer): + text, conf = predictor.recognize(variant) + else: + arr = preprocess_rec_input(variant) + input_handle.reshape(arr.shape) + input_handle.copy_from_cpu(arr) + predictor.run() + out = output_handle.copy_to_cpu()[0] + text, conf = decode_ctc(out, charset, charset_mask) + score = score_rec_text(text, conf, lexicon) if score > best_score: best_text = text best_conf = conf @@ -386,7 +555,7 @@ def normalize_for_digit_parsing(text: str) -> str: def parse_word_index_from_text(text: str, max_index: int = 12) -> int: - safe_max_index = max(1, min(24, int(max_index))) + safe_max_index = max(1, min(33, int(max_index))) normalized = text.strip() if not normalized: return -1 @@ -424,7 +593,7 @@ def parse_word_index_from_text(text: str, max_index: int = 12) -> int: def detect_verify_word_index(candidates: Sequence[str], max_index: int = 12) -> int: - safe_max_index = max(1, min(24, int(max_index))) + safe_max_index = max(1, min(33, int(max_index))) clean_candidates = [str(value).strip() for value in candidates if str(value).strip()] if not clean_candidates: return -1 @@ -550,23 +719,24 @@ def recognize_verify_number(image_bgr: np.ndarray, max_index: int = 12) -> Tuple return output_text, confidence, len(texts), detected_index -def detect_text_boxes( - image_bgr: np.ndarray, - mask_top_ratio: float = 0.14, - mask_bottom_ratio: float = 0.90, -) -> List[Tuple[int, int, int, int]]: - h, w = image_bgr.shape[:2] - gray = cv2.cvtColor(image_bgr, cv2.COLOR_BGR2GRAY) +def flatten_illumination(gray: np.ndarray) -> np.ndarray: + """Divides out the low-frequency illumination background (flat-field). - # Remove top title and bottom button band for stable text-box extraction. - work = gray.copy() - if mask_top_ratio > 0: - work[: int(h * mask_top_ratio), :] = 0 - if mask_bottom_ratio < 1: - work[int(h * mask_bottom_ratio) :, :] = 0 + Screen-camera captures are rarely evenly lit — glare and corner falloff make + a single global threshold split the page wrong. Dividing by a heavily blurred + copy normalizes brightness so Otsu sees uniform text-vs-background contrast. + """ + h, w = gray.shape[:2] + sigma = max(8.0, min(h, w) * 0.05) + background = cv2.GaussianBlur(gray, (0, 0), sigma) + background = np.maximum(background, 1) + flattened = cv2.divide(gray, background, scale=128) + return cv2.normalize(flattened, None, 0, 255, cv2.NORM_MINMAX).astype(np.uint8) - _, binary = cv2.threshold(work, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU) +def boxes_from_binary( + binary: np.ndarray, w: int, h: int +) -> List[Tuple[int, int, int, int]]: kernel_w = max(17, int(round(w * 0.03))) kernel_h = max(3, int(round(h * 0.004))) kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (kernel_w, kernel_h)) @@ -587,6 +757,32 @@ def detect_text_boxes( return boxes +def detect_text_boxes( + image_bgr: np.ndarray, + mask_top_ratio: float = 0.14, + mask_bottom_ratio: float = 0.90, +) -> List[Tuple[int, int, int, int]]: + h, w = image_bgr.shape[:2] + gray = cv2.cvtColor(image_bgr, cv2.COLOR_BGR2GRAY) + + # Try illumination-flattened Otsu first (robust to glare / uneven lighting), + # keep plain global Otsu as a second opinion, and use whichever binarization + # recovers more text boxes — missing boxes is the failure mode that aborts + # grid recognition, while spurious ones are removed by the size filters. + candidates: List[List[Tuple[int, int, int, int]]] = [] + for source in (flatten_illumination(gray), gray): + work = source.copy() + # Remove top title and bottom button band for stable text-box extraction. + if mask_top_ratio > 0: + work[: int(h * mask_top_ratio), :] = 0 + if mask_bottom_ratio < 1: + work[int(h * mask_bottom_ratio) :, :] = 0 + _, binary = cv2.threshold(work, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU) + candidates.append(boxes_from_binary(binary, w, h)) + + return max(candidates, key=len) + + def cluster_rows( boxes: Sequence[Tuple[int, int, int, int]], image_h: int, @@ -622,6 +818,65 @@ def merge_boxes(items: Sequence[Tuple[int, int, int, int]]) -> Optional[Tuple[in MNEMONIC_GRID_WORD_COUNTS = {12: 6, 18: 9, 20: 10, 24: 12} +def grid_column_anchors( + rows_items: Sequence[Sequence[Tuple[int, int, int, int]]], + median_h: float, +) -> List[float]: + """Median x-start of the index and word columns across grid rows. + + The device UI is a fixed grid, so the "N." box and the word box start at + nearly the same x in every row; glare boxes do not line up with either. + """ + firsts: List[float] = [] + seconds: List[float] = [] + for items in rows_items: + ordered = sorted( + (item for item in items if item[3] <= median_h * 1.8), + key=lambda box: box[0], + ) or sorted(items, key=lambda box: box[0]) + if ordered: + firsts.append(float(ordered[0][0])) + if len(ordered) > 1: + seconds.append(float(ordered[1][0])) + anchors: List[float] = [] + if firsts: + anchors.append(float(statistics.median(firsts))) + if seconds: + anchors.append(float(statistics.median(seconds))) + return anchors + + +def filter_grid_cell_items( + items: Sequence[Tuple[int, int, int, int]], + median_h: float, + col_x_anchors: Sequence[float], +) -> List[Tuple[int, int, int, int]]: + """Drop glare/reflection det boxes from one grid cell before merging. + + Screen-edge glare produces boxes that merge into the cell and corrupt the + recognized word (e.g. "board" -> "maboardowb"). Real boxes either start at a + column anchor (index or word column) or directly continue the previous box + (a word det split into fragments); everything else is noise. + """ + text_like = [item for item in items if item[3] <= median_h * 1.8] + if not text_like: + return list(items) + text_like.sort(key=lambda box: box[0]) + if not col_x_anchors: + return text_like + tol = median_h * 1.5 + continuation_gap = median_h * 0.45 + kept: List[Tuple[int, int, int, int]] = [] + for box in text_like: + aligned = any(abs(box[0] - anchor) <= tol for anchor in col_x_anchors) + continues_previous = bool(kept) and ( + box[0] - (kept[-1][0] + kept[-1][2]) <= continuation_gap + ) + if aligned or continues_previous: + kept.append(box) + return kept or text_like + + def recognize_mnemonic_grid( image_bgr: np.ndarray, rows: Sequence[Dict[str, Any]], @@ -631,6 +886,8 @@ def recognize_mnemonic_grid( charset: Sequence[str], num_rows: int = 6, variant_count: int = REC_VARIANT_COUNT, + lexicon: Optional[Lexicon] = None, + charset_mask: Optional[np.ndarray] = None, ) -> Optional[Tuple[str, float]]: """Recognize a 2-column mnemonic grid with `num_rows` rows (num_rows*2 words total). @@ -648,20 +905,35 @@ def recognize_mnemonic_grid( x_mid = image_w / 2.0 lines: List[str] = [] confs: List[float] = [] + all_heights = [item[3] for row in dense_rows for item in row["items"]] + median_h = float(statistics.median(all_heights)) if all_heights else 0.0 - for row_idx, row in enumerate(dense_rows): - left_items = [item for item in row["items"] if item[0] + item[2] / 2.0 < x_mid] - right_items = [item for item in row["items"] if item[0] + item[2] / 2.0 >= x_mid] + split_rows = [ + ( + [item for item in row["items"] if item[0] + item[2] / 2.0 < x_mid], + [item for item in row["items"] if item[0] + item[2] / 2.0 >= x_mid], + ) + for row in dense_rows + ] + left_anchors = grid_column_anchors([left for left, _ in split_rows], median_h) + right_anchors = grid_column_anchors([right for _, right in split_rows], median_h) + + for row_idx, (left_items, right_items) in enumerate(split_rows): + if median_h > 0: + left_items = filter_grid_cell_items(left_items, median_h, left_anchors) + right_items = filter_grid_cell_items(right_items, median_h, right_anchors) left_box = merge_boxes(left_items) right_box = merge_boxes(right_items) if left_box is None or right_box is None: return None left_text, left_conf = recognize_crop( - image_bgr, left_box, predictor, input_handle, output_handle, charset, variant_count + image_bgr, left_box, predictor, input_handle, output_handle, charset, + variant_count, lexicon, charset_mask ) right_text, right_conf = recognize_crop( - image_bgr, right_box, predictor, input_handle, output_handle, charset, variant_count + image_bgr, right_box, predictor, input_handle, output_handle, charset, + variant_count, lexicon, charset_mask ) left_token = extract_alpha_token(left_text) @@ -683,6 +955,30 @@ def recognize_mnemonic_grid( return "\n".join(lines), avg_conf +def generic_row_anchors( + rows: Sequence[Dict[str, Any]], + median_h: float, +) -> List[float]: + """Cluster box x-starts that repeat across rows (fixed-layout list columns).""" + clusters: List[Dict[str, Any]] = [] + tol = median_h * 1.5 + for row_idx, row in enumerate(rows): + for item in row["items"]: + if item[3] > median_h * 1.8: + continue + x = float(item[0]) + for cluster in clusters: + if abs(x - cluster["center"]) <= tol: + cluster["xs"].append(x) + cluster["rows"].add(row_idx) + cluster["center"] = float(statistics.median(cluster["xs"])) + break + else: + clusters.append({"center": x, "xs": [x], "rows": {row_idx}}) + min_rows = max(3, int(len(rows) * 0.4)) + return [cluster["center"] for cluster in clusters if len(cluster["rows"]) >= min_rows] + + def recognize_generic_lines( image_bgr: np.ndarray, rows: Sequence[Dict[str, Any]], @@ -691,25 +987,75 @@ def recognize_generic_lines( output_handle: Any, charset: Sequence[str], variant_count: int = REC_VARIANT_COUNT, + lexicon: Optional[Lexicon] = None, + charset_mask: Optional[np.ndarray] = None, + grid_anchor_filter: bool = False, ) -> Tuple[str, float]: + split_columns = False + if grid_anchor_filter and len(rows) >= 4: + # Scrolled list windows clip the first/last visible row at the DEVICE + # viewport edge (not our crop edge — wider candidate crops shift it inward), + # leaving half-height glyphs that OCR as garbage which can steal a word + # index renderer-side (e.g. "1. ens" overwriting the real word 1). A real + # row has at least one full-height box; drop rows that are all-short or + # touch the image edge. + image_h = image_bgr.shape[0] + heights = [item[3] for row in rows for item in row["items"]] + median_h = float(statistics.median(heights)) if heights else 0.0 + rows = [ + row for row in rows + if min(item[1] for item in row["items"]) > 2 + and max(item[1] + item[3] for item in row["items"]) < image_h - 2 + # Real rows always contain a full-height digit box (the "N." index), so + # their tallest box sits at ~1.0x the median; a viewport-clipped row is + # uniformly shortened. + and (median_h <= 0 or max(item[3] for item in row["items"]) >= median_h * 0.78) + ] + # Scrolled mnemonic windows (20/24-word pages) can't use the fixed grid, + # but their columns still align across rows — use that to drop glare boxes + # before merging each row (same failure mode as the grid path). + heights = [item[3] for row in rows for item in row["items"]] + median_h = float(statistics.median(heights)) if heights else 0.0 + anchors = generic_row_anchors(rows, median_h) if median_h > 0 else [] + if anchors: + rows = [ + {**row, "items": filter_grid_cell_items(row["items"], median_h, anchors)} + for row in rows + ] + # Recognize the two grid columns separately: a whole-row crop makes the + # rec model glue the left word to the right index ("gym13. stove"), which + # the renderer-side index parser can't split reliably. + split_columns = True + + x_mid = image_bgr.shape[1] / 2.0 lines: List[str] = [] confs: List[float] = [] for row in rows: - box = merge_boxes(row["items"]) - if box is None: - continue - text, conf = recognize_crop( - image_bgr, box, predictor, input_handle, output_handle, charset, variant_count - ) - if text.strip(): - lines.append(text.strip()) - confs.append(conf) + if split_columns: + cell_groups = [ + [item for item in row["items"] if item[0] + item[2] / 2.0 < x_mid], + [item for item in row["items"] if item[0] + item[2] / 2.0 >= x_mid], + ] + else: + cell_groups = [list(row["items"])] + for cell_items in cell_groups: + box = merge_boxes(cell_items) + if box is None: + continue + text, conf = recognize_crop( + image_bgr, box, predictor, input_handle, output_handle, charset, + variant_count, lexicon, charset_mask + ) + if text.strip(): + lines.append(text.strip()) + confs.append(conf) if not lines: # Last fallback: recognize the whole image as one line. h, w = image_bgr.shape[:2] text, conf = recognize_crop( - image_bgr, (0, 0, w, h), predictor, input_handle, output_handle, charset, variant_count + image_bgr, (0, 0, w, h), predictor, input_handle, output_handle, charset, + variant_count, lexicon, charset_mask ) return text.strip(), conf * 100.0 @@ -735,14 +1081,16 @@ def infer_once(payload: Dict[str, Any]) -> Dict[str, Any]: expected_word_count = int(raw_expected_word_count) except (TypeError, ValueError): expected_word_count = 0 - if expected_word_count not in {12, 18, 20, 24}: + if expected_word_count not in {12, 18, 20, 24, 33}: expected_word_count = 0 try: rec_variant_count = int(payload.get("recVariantCount") or REC_VARIANT_COUNT) except (TypeError, ValueError): rec_variant_count = REC_VARIANT_COUNT rec_variant_count = max(1, min(4, rec_variant_count)) - verify_max_index = expected_word_count if expected_word_count in {12, 18, 20, 24} else 12 + verify_max_index = expected_word_count if expected_word_count in {12, 18, 20, 24, 33} else 12 + wordlist_hint = str(payload.get("wordlistHint") or "").strip().lower() + wordlist_name = wordlist_hint if wordlist_hint in LEXICON_FILES else "bip39" if layout_hint in {"verify-number", "verify_number"}: try: @@ -767,7 +1115,8 @@ def infer_once(payload: Dict[str, Any]) -> Dict[str, Any]: boxes = detect_text_boxes(image_bgr, mask_top_ratio=0.0, mask_bottom_ratio=1.0) rows = cluster_rows(boxes, image_bgr.shape[0]) text, confidence = recognize_generic_lines( - image_bgr, rows, predictor, input_handle, output_handle, charset, rec_variant_count + image_bgr, rows, predictor, input_handle, output_handle, charset, rec_variant_count, + charset_mask=build_charset_mask(charset, "alnum-index"), ) detected_index = parse_word_index_from_text(text, verify_max_index) if detected_index != -1 and f"#{detected_index}" not in text: @@ -789,6 +1138,27 @@ def infer_once(payload: Dict[str, Any]) -> Dict[str, Any]: predictor, input_handle, output_handle, charset = ensure_rec_model(resolve_en_model_dir()) mnemonic_layout = layout_hint in {"mnemonic", "mnemonic-grid", "mnemonic_words"} verify_options_layout = layout_hint in {"verify-options", "verify_options"} + if mnemonic_layout: + # The device renders cyan/blue text; glare from warm ambient light lives + # mostly in the red/green channels. Keeping only the blue channel keeps + # glyphs bright while suppressing glare clouds that wash them out (plain + # grayscale mixes the glare back in). Verified to recover words under + # reflections; do NOT apply to verify layouts — it can break their + # detection on some crops. + blue_channel = image_bgr[:, :, 0] + image_bgr = cv2.merge([blue_channel, blue_channel, blue_channel]) + # Mnemonic and verify-option words always come from a closed wordlist; use it + # to pick the best preprocessing variant per crop. + lexicon = load_lexicon(wordlist_name) if (mnemonic_layout or verify_options_layout) else None + # Grid cells are always wordlist words (indices come from row order), so the + # grid decode is letters-only; generic lines keep digits for "1."/"#N" indices + # unless the layout says the region contains only words. + letters_mask = build_charset_mask(charset, "letters") + generic_mask = ( + letters_mask + if (mnemonic_layout or verify_options_layout) + else build_charset_mask(charset, "alnum-index") + ) # Mnemonic and verify-option crops are already tight; keep full vertical content. use_full_vertical_mask = mnemonic_layout or verify_options_layout or expected_word_count >= 18 boxes = detect_text_boxes( @@ -811,6 +1181,8 @@ def infer_once(payload: Dict[str, Any]) -> Dict[str, Any]: image_bgr, rows, predictor, input_handle, output_handle, charset, num_rows=num_rows, variant_count=rec_variant_count, + lexicon=lexicon, + charset_mask=letters_mask, ) else: # Unknown word count: try all supported layouts in ascending order. @@ -819,6 +1191,8 @@ def infer_once(payload: Dict[str, Any]) -> Dict[str, Any]: image_bgr, rows, predictor, input_handle, output_handle, charset, num_rows=num_rows, variant_count=rec_variant_count, + lexicon=lexicon, + charset_mask=letters_mask, ) if mnemonic_result is not None: break @@ -841,6 +1215,8 @@ def infer_once(payload: Dict[str, Any]) -> Dict[str, Any]: image_bgr, retry_rows, predictor, input_handle, output_handle, charset, num_rows=retry_num_rows, variant_count=rec_variant_count, + lexicon=lexicon, + charset_mask=letters_mask, ) if retry_result is not None: boxes = retry_boxes @@ -855,7 +1231,9 @@ def infer_once(payload: Dict[str, Any]) -> Dict[str, Any]: mode = "mnemonic-grid" else: text, confidence = recognize_generic_lines( - image_bgr, rows, predictor, input_handle, output_handle, charset, rec_variant_count + image_bgr, rows, predictor, input_handle, output_handle, charset, + rec_variant_count, lexicon, generic_mask, + grid_anchor_filter=mnemonic_layout, ) return { @@ -882,7 +1260,7 @@ def run_once(): def run_server(): ensure_rec_model(resolve_en_model_dir()) - print(json.dumps({"type": "ready"}, ensure_ascii=False)) + print(json.dumps({"type": "ready", "scriptVersion": SCRIPT_VERSION}, ensure_ascii=False)) sys.stdout.flush() for line in sys.stdin: diff --git a/scripts/requirements.txt b/scripts/requirements.txt index 35d0388..78b2c5e 100644 --- a/scripts/requirements.txt +++ b/scripts/requirements.txt @@ -5,7 +5,8 @@ # Or manually: pip install -r scripts/requirements.txt paddlepaddle==3.0.0 -paddleocr>=2.9.0 +paddleocr>=3.7.0 +onnxruntime>=1.17.0 opencv-python>=4.9.0 Pillow>=10.0.0 PyYAML>=6.0 diff --git a/scripts/setup_ocr.sh b/scripts/setup_ocr.sh index 883cd31..0f97c37 100755 --- a/scripts/setup_ocr.sh +++ b/scripts/setup_ocr.sh @@ -73,9 +73,10 @@ echo "⬇️ Installing OCR dependencies (this may take a few minutes)..." # ── Smoke-test the install ──────────────────────────────────────────────────── echo "🧪 Verifying installation..." "$VENV_PYTHON" -c " -import paddle, cv2, numpy, PIL, yaml, paddleocr +import paddle, cv2, numpy, PIL, yaml, paddleocr, onnxruntime print(f' paddle {paddle.__version__}') print(f' paddleocr {paddleocr.__version__}') +print(f' onnxruntime {onnxruntime.__version__}') print(f' cv2 {cv2.__version__}') print(f' numpy {numpy.__version__}') print(f' Pillow {PIL.__version__}') diff --git a/scripts/slip39_english.txt b/scripts/slip39_english.txt new file mode 100644 index 0000000..5673e7c --- /dev/null +++ b/scripts/slip39_english.txt @@ -0,0 +1,1024 @@ +academic +acid +acne +acquire +acrobat +activity +actress +adapt +adequate +adjust +admit +adorn +adult +advance +advocate +afraid +again +agency +agree +aide +aircraft +airline +airport +ajar +alarm +album +alcohol +alien +alive +alpha +already +alto +aluminum +always +amazing +ambition +amount +amuse +analysis +anatomy +ancestor +ancient +angel +angry +animal +answer +antenna +anxiety +apart +aquatic +arcade +arena +argue +armed +artist +artwork +aspect +auction +august +aunt +average +aviation +avoid +award +away +axis +axle +beam +beard +beaver +become +bedroom +behavior +being +believe +belong +benefit +best +beyond +bike +biology +birthday +bishop +black +blanket +blessing +blimp +blind +blue +body +bolt +boring +born +both +boundary +bracelet +branch +brave +breathe +briefing +broken +brother +browser +bucket +budget +building +bulb +bulge +bumpy +bundle +burden +burning +busy +buyer +cage +calcium +camera +campus +canyon +capacity +capital +capture +carbon +cards +careful +cargo +carpet +carve +category +cause +ceiling +center +ceramic +champion +change +charity +check +chemical +chest +chew +chubby +cinema +civil +class +clay +cleanup +client +climate +clinic +clock +clogs +closet +clothes +club +cluster +coal +coastal +coding +column +company +corner +costume +counter +course +cover +cowboy +cradle +craft +crazy +credit +cricket +criminal +crisis +critical +crowd +crucial +crunch +crush +crystal +cubic +cultural +curious +curly +custody +cylinder +daisy +damage +dance +darkness +database +daughter +deadline +deal +debris +debut +decent +decision +declare +decorate +decrease +deliver +demand +density +deny +depart +depend +depict +deploy +describe +desert +desire +desktop +destroy +detailed +detect +device +devote +diagnose +dictate +diet +dilemma +diminish +dining +diploma +disaster +discuss +disease +dish +dismiss +display +distance +dive +divorce +document +domain +domestic +dominant +dough +downtown +dragon +dramatic +dream +dress +drift +drink +drove +drug +dryer +duckling +duke +duration +dwarf +dynamic +early +earth +easel +easy +echo +eclipse +ecology +edge +editor +educate +either +elbow +elder +election +elegant +element +elephant +elevator +elite +else +email +emerald +emission +emperor +emphasis +employer +empty +ending +endless +endorse +enemy +energy +enforce +engage +enjoy +enlarge +entrance +envelope +envy +epidemic +episode +equation +equip +eraser +erode +escape +estate +estimate +evaluate +evening +evidence +evil +evoke +exact +example +exceed +exchange +exclude +excuse +execute +exercise +exhaust +exotic +expand +expect +explain +express +extend +extra +eyebrow +facility +fact +failure +faint +fake +false +family +famous +fancy +fangs +fantasy +fatal +fatigue +favorite +fawn +fiber +fiction +filter +finance +findings +finger +firefly +firm +fiscal +fishing +fitness +flame +flash +flavor +flea +flexible +flip +float +floral +fluff +focus +forbid +force +forecast +forget +formal +fortune +forward +founder +fraction +fragment +frequent +freshman +friar +fridge +friendly +frost +froth +frozen +fumes +funding +furl +fused +galaxy +game +garbage +garden +garlic +gasoline +gather +general +genius +genre +genuine +geology +gesture +glad +glance +glasses +glen +glimpse +goat +golden +graduate +grant +grasp +gravity +gray +greatest +grief +grill +grin +grocery +gross +group +grownup +grumpy +guard +guest +guilt +guitar +gums +hairy +hamster +hand 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+pistol +pitch +plains +plan +plastic +platform +playoff +pleasure +plot +plunge +practice +prayer +preach +predator +pregnant +premium +prepare +presence +prevent +priest +primary +priority +prisoner +privacy +prize +problem +process +profile +program +promise +prospect +provide +prune +public +pulse +pumps +punish +puny +pupal +purchase +purple +python +quantity +quarter +quick +quiet +race +racism +radar +railroad +rainbow +raisin +random +ranked +rapids +raspy +reaction +realize +rebound +rebuild +recall +receiver +recover +regret +regular +reject +relate +remember +remind +remove +render +repair +repeat +replace +require +rescue +research +resident +response +result +retailer +retreat +reunion +revenue +review +reward +rhyme +rhythm +rich +rival +river +robin +rocky +romantic +romp +roster +round +royal +ruin +ruler +rumor +sack +safari +salary +salon +salt +satisfy +satoshi +saver +says +scandal +scared +scatter +scene +scholar +science +scout +scramble +screw +script +scroll +seafood +season +secret +security +segment +senior +shadow +shaft +shame +shaped +sharp +shelter +sheriff +short +should +shrimp +sidewalk +silent +silver +similar +simple +single +sister +skin +skunk +slap +slavery +sled +slice +slim +slow +slush +smart +smear +smell +smirk +smith +smoking +smug +snake +snapshot +sniff +society +software +soldier +solution +soul +source +space +spark +speak +species +spelling +spend +spew +spider +spill +spine +spirit +spit +spray +sprinkle +square +squeeze +stadium +staff +standard +starting +station +stay +steady +step +stick +stilt +story +strategy +strike +style +subject +submit +sugar +suitable +sunlight +superior +surface +surprise +survive +sweater +swimming +swing +switch +symbolic +sympathy +syndrome +system +tackle +tactics +tadpole +talent +task +taste +taught +taxi +teacher +teammate +teaspoon +temple +tenant +tendency +tension +terminal +testify +texture +thank +that +theater +theory +therapy +thorn +threaten +thumb +thunder +ticket +tidy +timber +timely +ting +tofu +together +tolerate +total +toxic +tracks +traffic +training +transfer +trash +traveler +treat +trend +trial +tricycle +trip +triumph +trouble +true +trust +twice +twin +type +typical +ugly +ultimate +umbrella +uncover +undergo +unfair +unfold +unhappy +union +universe +unkind +unknown +unusual +unwrap +upgrade +upstairs +username +usher +usual +valid +valuable +vampire +vanish +various +vegan +velvet +venture +verdict +verify +very +veteran +vexed +victim +video +view +vintage +violence +viral +visitor +visual +vitamins +vocal +voice +volume +voter +voting +walnut +warmth +warn +watch +wavy +wealthy +weapon +webcam +welcome +welfare +western +width +wildlife +window +wine +wireless +wisdom +withdraw +wits +wolf +woman +work +worthy +wrap +wrist +writing +wrote +year +yelp +yield +yoga +zero diff --git a/src/components/CameraPanel.tsx b/src/components/CameraPanel.tsx index 04dab0f..b38cd7f 100644 --- a/src/components/CameraPanel.tsx +++ b/src/components/CameraPanel.tsx @@ -4,7 +4,11 @@ import { validateMnemonic } from '@scure/bip39'; import { wordlist as bip39English } from '@scure/bip39/wordlists/english.js'; import { slip39English } from '../slip39Wordlist'; import { DEVICE_OCR_SCENES, type SequenceOcrSceneConfig } from '../ocr/deviceScenes'; -import { getStoredDeviceTestSet } from '../deviceTestSetPreference'; +import { + getStoredDeviceTestSet, + normalizePreferredDeviceTestSet, + type PreferredDeviceTestSetId, +} from '../deviceTestSetPreference'; import { rotateVideoFrameToCanvas, cropToROI, @@ -13,6 +17,7 @@ import { VERIFY_NUMBER_SCENE, VERIFY_OPTIONS_SCENE, type OcrSceneConfig, + type ROI, } from '../ocr'; import './CameraPanel.css'; @@ -47,10 +52,6 @@ const MNEMONIC_OCR_PARAMS = { // Two-column mnemonic grid fits sparse text segmentation better than strict single-block. tessedit_pageseg_mode: OCR_PSM.SPARSE_TEXT, } as const; -const MNEMONIC_COLUMN_OCR_PARAMS = { - ...BASE_OCR_PARAMS, - tessedit_pageseg_mode: OCR_PSM.SINGLE_COLUMN, -} as const; const NUMBER_OCR_PARAMS = { ...BASE_OCR_PARAMS, tessedit_pageseg_mode: OCR_PSM.SINGLE_LINE, @@ -139,11 +140,13 @@ interface OcrTriggerOptions { allowPartial?: boolean; requireBip39?: boolean; sceneConfig?: SequenceOcrSceneConfig; + deviceTestSetId?: PreferredDeviceTestSetId; } interface VerifyOcrTriggerOptions { numberSceneConfig?: SequenceOcrSceneConfig; optionsSceneConfig?: SequenceOcrSceneConfig; + deviceTestSetId?: PreferredDeviceTestSetId; } type VideoWithFrameCallback = HTMLVideoElement & { @@ -162,28 +165,62 @@ function withOcrSceneDefaults( }; } -function getCurrentDeviceOcrScenes() { - return DEVICE_OCR_SCENES[getStoredDeviceTestSet()]; +function getDeviceOcrScenes(deviceTestSetId?: PreferredDeviceTestSetId | string | null) { + return DEVICE_OCR_SCENES[normalizePreferredDeviceTestSet(deviceTestSetId ?? getStoredDeviceTestSet())]; } -function getCurrentDeviceRecVariantCount(layoutHint?: 'mnemonic' | 'verify-options' | 'verify-number' | 'generic'): number | undefined { - return getStoredDeviceTestSet() === 'pro2' && (layoutHint === 'mnemonic' || layoutHint === 'verify-options') +function getDeviceRecVariantCount( + layoutHint?: 'mnemonic' | 'verify-options' | 'verify-number' | 'generic', + deviceTestSetId?: PreferredDeviceTestSetId | string | null +): number | undefined { + return normalizePreferredDeviceTestSet(deviceTestSetId ?? getStoredDeviceTestSet()) === 'pro2' + && (layoutHint === 'mnemonic' || layoutHint === 'verify-options') ? 4 : undefined; } -function getCurrentMnemonicSceneConfig(): OcrSceneConfig | undefined { +function resolveDeviceMnemonicScene( + deviceTestSetId?: PreferredDeviceTestSetId | string | null, + expectedWordCount: number = 12, + options?: { allowPartial?: boolean } +): { scene?: SequenceOcrSceneConfig; layoutSpecific: boolean } { + const createWallet = getDeviceOcrScenes(deviceTestSetId).createWallet; + // 12-word and 18-word lists fit one page; 20/24-word lists are a scrolled + // window captured in two passes (part1 = allowPartial first pass, part2 = + // the scrolled second pass). Fall back count-by-count so devices without a + // per-layout scene keep their generic framing; layoutSpecific says whether + // the scene was calibrated for this exact page layout (rescue-crop + // candidates are skipped when it was). + if (expectedWordCount <= 12) { + return { scene: createWallet?.mnemonic12, layoutSpecific: !!createWallet?.mnemonic12 }; + } + if (expectedWordCount <= 18) { + if (createWallet?.mnemonic18) return { scene: createWallet.mnemonic18, layoutSpecific: true }; + return { scene: createWallet?.mnemonic12, layoutSpecific: false }; + } + const partScene = options?.allowPartial + ? createWallet?.mnemonic24Part1 + : createWallet?.mnemonic24Part2 ?? createWallet?.mnemonic24Part1; + if (partScene) return { scene: partScene, layoutSpecific: true }; + return { scene: createWallet?.mnemonic18 ?? createWallet?.mnemonic12, layoutSpecific: false }; +} + +function getMnemonicSceneConfig( + deviceTestSetId?: PreferredDeviceTestSetId | string | null, + expectedWordCount: number = 12, + options?: { allowPartial?: boolean } +): OcrSceneConfig | undefined { return withOcrSceneDefaults( - getCurrentDeviceOcrScenes().createWallet?.mnemonic12, + resolveDeviceMnemonicScene(deviceTestSetId, expectedWordCount, options).scene, MNEMONIC_SCENE ); } -function getCurrentVerifySceneConfigs(): { +function getVerifySceneConfigs(deviceTestSetId?: PreferredDeviceTestSetId | string | null): { numberSceneConfig?: OcrSceneConfig; optionsSceneConfig?: OcrSceneConfig; } { - const verifyWalletScenes = getCurrentDeviceOcrScenes().verifyWallet; + const verifyWalletScenes = getDeviceOcrScenes(deviceTestSetId).verifyWallet; return { numberSceneConfig: withOcrSceneDefaults(verifyWalletScenes?.number, VERIFY_NUMBER_SCENE), optionsSceneConfig: withOcrSceneDefaults(verifyWalletScenes?.options, VERIFY_OPTIONS_SCENE), @@ -233,7 +270,8 @@ function parseMnemonicIndexToken(token: string, expectedWordCount: number): numb function buildMnemonicSceneCandidates( baseScene: OcrSceneConfig, - expectedWordCount: number + expectedWordCount: number, + layoutCalibrated: boolean = false ): OcrSceneConfig[] { if (expectedWordCount <= 12) { return [baseScene]; @@ -261,6 +299,29 @@ function buildMnemonicSceneCandidates( candidates.push(normalized); }; + if (layoutCalibrated) { + // A device-calibrated ROI already frames the full grid; keep the base plus + // one horizontal-only expansion against small framing drift. The + // wide/column-focused rescue candidates below exist for uncalibrated + // framings — they waste OCR passes here and a high-confidence partial crop + // (e.g. right column only) can hijack the displayed result image. Never + // expand vertically: calibrated tops/bottoms sit right against noise zones + // (instruction line, viewport-clipped scroll row, 继续 button), and pulling + // those back in produced phantom indexed words ("1. ens") that corrupted + // the part merge. + add(baseScene); + add({ + ...baseScene, + roi: { + x: Math.max(0, baseScene.roi.x - 16), + y: baseScene.roi.y, + width: baseScene.roi.width + 32, + height: baseScene.roi.height, + }, + }); + return candidates; + } + if (expectedWordCount >= 24) { // 24-word part1 needs stronger top coverage (first rows are easy to clip). add({ @@ -439,6 +500,23 @@ function getBip39Candidates(ocrWord: string, topN: number = 5): { word: string; return candidates.slice(0, topN); } +/** + * Infers which closed wordlist a stored mnemonic uses (BIP39 vs SLIP39). + * The two lists are distinct and must never be mixed; membership counting + * over the stored words picks the right one when the flow doesn't say. + */ +function inferWordlistHint(words: readonly string[] | undefined): 'bip39' | 'slip39' { + if (!words || words.length === 0) return 'bip39'; + let bipHits = 0; + let slipHits = 0; + for (const word of words) { + if (!word) continue; + if (bip39English.includes(word)) bipHits++; + if (slip39English.includes(word)) slipHits++; + } + return slipHits > bipHits ? 'slip39' : 'bip39'; +} + /** * Corrects an OCR word against the SLIP39 English wordlist. * Uses the same prefix-match tie-breaking as correctToBip39: @@ -594,6 +672,122 @@ function tryBip39AutoCorrect(mnemonicWords: MnemonicWord[]): boolean { return false; } +interface MnemonicConsensus { + mnemonicWords: MnemonicWord[]; + hasCompleteSequence: boolean; + bip39Valid: boolean; + missingIndices: number[]; +} + +/** + * Accumulates per-index word votes across multiple OCR attempts (frames) and + * builds a consensus mnemonic. Camera noise is frame-independent, so majority + * voting across frames removes most single-frame recognition errors, and slots + * missed in one frame can be filled by another. + */ +function createMnemonicVoteAccumulator(expectedWordCount: number, applyBip39Wordlist: boolean) { + const votes = new Map>(); + let attemptCount = 0; + + const add = (words: MnemonicWord[]): void => { + attemptCount++; + for (const item of words) { + if (item.index < 1 || item.index > expectedWordCount || !item.word) continue; + let byWord = votes.get(item.index); + if (!byWord) { + byWord = new Map(); + votes.set(item.index, byWord); + } + const entry = byWord.get(item.word); + if (!entry) { + byWord.set(item.word, { count: 1, confSum: item.wordConfidence, best: item }); + } else { + entry.count++; + entry.confSum += item.wordConfidence; + if (item.wordConfidence > entry.best.wordConfidence) entry.best = item; + } + } + }; + + const consensus = (): MnemonicConsensus | null => { + // With a single frame, consensus degenerates to that frame's result. + if (attemptCount < 2) return null; + + const mnemonicWords: MnemonicWord[] = []; + const missingIndices: number[] = []; + for (let index = 1; index <= expectedWordCount; index++) { + const byWord = votes.get(index); + if (!byWord || byWord.size === 0) { + missingIndices.push(index); + continue; + } + let winner: { count: number; confSum: number; best: MnemonicWord } | null = null; + for (const entry of byWord.values()) { + if ( + !winner + || entry.count > winner.count + || (entry.count === winner.count && entry.confSum > winner.confSum) + ) { + winner = entry; + } + } + if (winner) mnemonicWords.push({ ...winner.best }); + } + + const hasCompleteSequence = missingIndices.length === 0 + && mnemonicWords.length === expectedWordCount; + let bip39Valid = false; + if (hasCompleteSequence && applyBip39Wordlist) { + const phrase = mnemonicWords.map((w) => w.word).join(' '); + try { + bip39Valid = validateMnemonic(phrase, bip39English); + } catch { + bip39Valid = false; + } + if (!bip39Valid && ENABLE_CHECKSUM_GUIDED_AUTOCORRECT) { + bip39Valid = tryBip39AutoCorrect(mnemonicWords); + } + } else if (hasCompleteSequence && !applyBip39Wordlist) { + const anyInvalid = mnemonicWords.some((w) => !slip39English.includes(w.word)); + if (anyInvalid) { + trySlip39AutoCorrect(mnemonicWords); + } + } + + return { mnemonicWords, hasCompleteSequence, bip39Valid, missingIndices }; + }; + + return { add, consensus }; +} + +/** Reference frame that all ROI configs are authored against (rotated portrait capture). */ +const ROI_REFERENCE_FRAME = { width: 1080, height: 1920 } as const; + +/** + * Scales an ROI authored against the 1080x1920 reference frame to the actual + * captured frame size. getUserMedia's resolution is only an "ideal" hint, so a + * camera delivering e.g. 720p would otherwise crop the wrong physical region. + */ +function scaleRoiToFrame(roi: ROI, frameWidth: number, frameHeight: number): ROI { + if ( + (frameWidth === ROI_REFERENCE_FRAME.width && frameHeight === ROI_REFERENCE_FRAME.height) + || frameWidth <= 0 + || frameHeight <= 0 + ) { + return roi; + } + const scaleX = frameWidth / ROI_REFERENCE_FRAME.width; + const scaleY = frameHeight / ROI_REFERENCE_FRAME.height; + const x = Math.min(Math.round(roi.x * scaleX), Math.max(0, frameWidth - 1)); + const y = Math.min(Math.round(roi.y * scaleY), Math.max(0, frameHeight - 1)); + return { + x, + y, + width: Math.max(1, Math.min(Math.round(roi.width * scaleX), frameWidth - x)), + height: Math.max(1, Math.min(Math.round(roi.height * scaleY), frameHeight - y)), + }; +} + /** * Builds ROI crop + scaled OCR input image from a rotated source frame. */ @@ -601,8 +795,11 @@ function buildPreOcrCanvasFromFrame( sourceCanvas: HTMLCanvasElement, ocrCanvas: HTMLCanvasElement, sceneConfig: OcrSceneConfig -): { imageDataUrl: string; preOcrImageDataUrl: string } | null { - const cropCanvas = cropToROI(sourceCanvas, sceneConfig.roi); +): { imageDataUrl: string; preOcrImageDataUrl: string; cropCanvas: HTMLCanvasElement } | null { + const cropCanvas = cropToROI( + sourceCanvas, + scaleRoiToFrame(sceneConfig.roi, sourceCanvas.width, sourceCanvas.height) + ); const scaledCanvas = scaleCanvas(cropCanvas, sceneConfig.scale, sceneConfig.useNearestNeighbor); const ocrCtx = ocrCanvas.getContext('2d'); if (!ocrCtx) return null; @@ -612,7 +809,7 @@ function buildPreOcrCanvasFromFrame( ocrCtx.drawImage(scaledCanvas, 0, 0); const imageDataUrl = cropCanvas.toDataURL('image/jpeg', OCR_JPEG_QUALITY); const preOcrImageDataUrl = ocrCanvas.toDataURL('image/jpeg', OCR_JPEG_QUALITY); - return { imageDataUrl, preOcrImageDataUrl }; + return { imageDataUrl, preOcrImageDataUrl, cropCanvas }; } /** @@ -623,22 +820,21 @@ function buildPreOcrCanvasFromVideo( canvas: HTMLCanvasElement, ocrCanvas: HTMLCanvasElement, sceneConfig: OcrSceneConfig -): { imageDataUrl: string; preOcrImageDataUrl: string } | null { +): { imageDataUrl: string; preOcrImageDataUrl: string; cropCanvas: HTMLCanvasElement } | null { rotateVideoFrameToCanvas(video, canvas); return buildPreOcrCanvasFromFrame(canvas, ocrCanvas, sceneConfig); } /** * Converts OCR input canvas to PaddleOCR input data URL. - * Downscales oversized images to reduce inference cost. + * Uses lossless PNG: JPEG ringing around sharp OLED text edges hurts recognition, + * and the image only travels over local IPC. Downscales oversized images with + * smoothing to avoid nearest-neighbor pixel dropping. */ -function buildPaddleInputDataUrl( - sourceCanvas: HTMLCanvasElement, - useNearestNeighbor: boolean -): string { +function buildPaddleInputDataUrl(sourceCanvas: HTMLCanvasElement): string { const currentMaxSide = Math.max(sourceCanvas.width, sourceCanvas.height); if (currentMaxSide <= PADDLE_MAX_IMAGE_SIDE) { - return sourceCanvas.toDataURL('image/jpeg', OCR_JPEG_QUALITY); + return sourceCanvas.toDataURL('image/png'); } const ratio = PADDLE_MAX_IMAGE_SIDE / currentMaxSide; @@ -649,13 +845,13 @@ function buildPaddleInputDataUrl( resized.height = targetHeight; const ctx = resized.getContext('2d'); if (!ctx) { - return sourceCanvas.toDataURL('image/jpeg', OCR_JPEG_QUALITY); + return sourceCanvas.toDataURL('image/png'); } - ctx.imageSmoothingEnabled = !useNearestNeighbor; - ctx.imageSmoothingQuality = useNearestNeighbor ? 'low' : 'high'; + ctx.imageSmoothingEnabled = true; + ctx.imageSmoothingQuality = 'high'; ctx.drawImage(sourceCanvas, 0, 0, targetWidth, targetHeight); - return resized.toDataURL('image/jpeg', OCR_JPEG_QUALITY); + return resized.toDataURL('image/png'); } /** @@ -672,7 +868,7 @@ function captureRotatedFrameCanvas(video: HTMLVideoElement): HTMLCanvasElement { * Priority: digits after "#" -> "单词#N"/"word #N" patterns -> any valid digit in range. */ function extractWordIndexFromText(text: string, maxIndex: number = 12): number { - const safeMaxIndex = Math.max(1, Math.min(24, Math.floor(maxIndex))); + const safeMaxIndex = Math.max(1, Math.min(33, Math.floor(maxIndex))); const normalized = text.trim(); if (!normalized) return -1; const normalizedForDigits = normalized @@ -814,6 +1010,7 @@ function CameraPanel() { const [numberImageUrl, setNumberImageUrl] = useState(null); const [numberPreOcrImageUrl, setNumberPreOcrImageUrl] = useState(null); const ocrWorkerRef = useRef(null); + const latestMnemonicPreOcrImageRef = useRef(null); const saveStoredMnemonic = useCallback((words: string[], confidences?: number[]) => { const normalizedConfidences = Array.isArray(confidences) && confidences.length === words.length @@ -986,6 +1183,8 @@ function CameraPanel() { layoutHint?: 'mnemonic' | 'verify-options' | 'verify-number' | 'generic'; forceTesseract?: boolean; expectedWordCount?: number; + deviceTestSetId?: PreferredDeviceTestSetId; + wordlistHint?: 'bip39' | 'slip39'; } ): Promise<{ rawText: string; confidence: number; imageDataUrl: string; preOcrImageDataUrl: string; words: OcrWord[] } | null> => { const video = videoRef.current; @@ -1001,12 +1200,12 @@ function CameraPanel() { : buildPreOcrCanvasFromVideo(video, canvas, ocrCanvas, sceneConfig); if (!built) return null; const { imageDataUrl, preOcrImageDataUrl } = built; + // Paddle gets the raw ROI crop: the 5x upscale only helps Tesseract, and + // upscale -> downscale round trips plus JPEG re-encoding degrade the input. const ocrInputDataUrl = OCR_BACKEND === 'paddleocr_en' - ? buildPaddleInputDataUrl(ocrCanvas, sceneConfig.useNearestNeighbor) + ? buildPaddleInputDataUrl(built.cropCanvas) : preOcrImageDataUrl; - options?.onImageForOcr?.(ocrInputDataUrl); - const inferredLayoutHint = (() => { if (options?.layoutHint) return options.layoutHint; if ( @@ -1024,12 +1223,18 @@ function CameraPanel() { return 'generic' as const; })(); + if (inferredLayoutHint === 'mnemonic') { + latestMnemonicPreOcrImageRef.current = ocrInputDataUrl; + } + options?.onImageForOcr?.(ocrInputDataUrl); + if (!options?.forceTesseract && OCR_BACKEND === 'paddleocr_en' && window.electronAPI?.paddleOcrEnRecognize) { const paddle = await window.electronAPI.paddleOcrEnRecognize( ocrInputDataUrl, inferredLayoutHint, options?.expectedWordCount, - getCurrentDeviceRecVariantCount(inferredLayoutHint) + getDeviceRecVariantCount(inferredLayoutHint, options?.deviceTestSetId), + options?.wordlistHint ); const fallbackConfidence = Number.isFinite(paddle.confidence) ? paddle.confidence : 0; return { @@ -1091,8 +1296,9 @@ function CameraPanel() { frameCanvas?: HTMLCanvasElement; maxIndex?: number; sceneConfig?: OcrSceneConfig; + deviceTestSetId?: PreferredDeviceTestSetId; }): Promise<{ number: number; rawText: string; confidence: number; imageDataUrl: string; preOcrImageDataUrl: string } | null> => { - const maxIndex = Math.max(1, Math.min(24, Math.floor(options?.maxIndex ?? 12))); + const maxIndex = Math.max(1, Math.min(33, Math.floor(options?.maxIndex ?? 12))); const baseScene = options?.sceneConfig ?? VERIFY_NUMBER_SCENE; const fallbackScenes: OcrSceneConfig[] = [ // Tight first-line crop (often improves "#N" visibility). @@ -1213,6 +1419,7 @@ function CameraPanel() { ocrParams: OCR_BACKEND === 'tesseract' ? NUMBER_OCR_PARAMS : undefined, layoutHint: 'verify-number', expectedWordCount: maxIndex, + deviceTestSetId: options?.deviceTestSetId, }); if (!result) continue; @@ -1268,6 +1475,7 @@ function CameraPanel() { ocrParams: NUMBER_OCR_PARAMS, layoutHint: 'verify-number', expectedWordCount: maxIndex, + deviceTestSetId: options?.deviceTestSetId, }); if (!tesseractResult) continue; @@ -1325,6 +1533,8 @@ function CameraPanel() { frameCanvas?: HTMLCanvasElement; targetWord?: string; sceneConfig?: OcrSceneConfig; + deviceTestSetId?: PreferredDeviceTestSetId; + wordlistHint?: 'bip39' | 'slip39'; }): Promise<{ rawText: string; rawOptions: string[]; @@ -1370,6 +1580,8 @@ function CameraPanel() { const result = await runOcrOnRegion(scene, { frameCanvas: options?.frameCanvas, layoutHint: 'verify-options', + deviceTestSetId: options?.deviceTestSetId, + wordlistHint: options?.wordlistHint, }); if (!result) continue; @@ -1425,9 +1637,12 @@ function CameraPanel() { const runSingleOcr = useCallback(async (options?: { onImageForOcr?: (dataUrl: string) => void; sceneConfig?: OcrSceneConfig; + /** Scene is calibrated for this exact page layout: skip rescue-crop candidates. */ + sceneCalibrated?: boolean; frameCanvas?: HTMLCanvasElement; expectedWordCount?: number; applyBip39Wordlist?: boolean; + deviceTestSetId?: PreferredDeviceTestSetId; }): Promise<{ mnemonicWords: MnemonicWord[]; rawText: string; @@ -1441,11 +1656,15 @@ function CameraPanel() { const sceneConfig = options?.sceneConfig ?? MNEMONIC_SCENE; const expectedWordCount = Math.max( 12, - Math.min(24, Math.floor(options?.expectedWordCount ?? EXPECTED_MNEMONIC_COUNT)) + Math.min(33, Math.floor(options?.expectedWordCount ?? EXPECTED_MNEMONIC_COUNT)) ); const applyBip39Wordlist = options?.applyBip39Wordlist ?? true; const applySlip39Wordlist = !applyBip39Wordlist; // SLIP39 flow uses its own wordlist - const sceneCandidates = buildMnemonicSceneCandidates(sceneConfig, expectedWordCount); + const sceneCandidates = buildMnemonicSceneCandidates( + sceneConfig, + expectedWordCount, + options?.sceneCalibrated ?? false + ); // Extract numbered mnemonic words. const mnemonicPattern = new RegExp( @@ -1480,8 +1699,7 @@ function CameraPanel() { const upsertCandidateByConfidence = ( index: number, ocrWord: string, - wordConf: number, - mode: 'default' | 'column-row' = 'default' + wordConf: number ) => { if (index < 1 || index > expectedWordCount) return; const normalizedWord = ocrWord.toLowerCase(); @@ -1497,18 +1715,7 @@ function CameraPanel() { wordConfidence: wordConf, }; const existing = indexedWords.get(index); - if (!existing) { - indexedWords.set(index, candidate); - return; - } - if (mode === 'column-row') { - const shouldReplace = candidate.wordConfidence >= existing.wordConfidence * 0.85; - if (shouldReplace) { - indexedWords.set(index, candidate); - } - return; - } - if (candidate.wordConfidence > existing.wordConfidence) { + if (!existing || candidate.wordConfidence > existing.wordConfidence) { indexedWords.set(index, candidate); } }; @@ -1521,7 +1728,7 @@ function CameraPanel() { ) => { const normalizedWord = ocrWord.toLowerCase(); const wordConf = confidenceMap.get(normalizedWord) ?? fallbackConfidence; - upsertCandidateByConfidence(index, normalizedWord, wordConf, 'default'); + upsertCandidateByConfidence(index, normalizedWord, wordConf); }; const extractIndexedFromText = ( @@ -1586,104 +1793,6 @@ function CameraPanel() { } }; - const extractColumnWordsByBoxes = ( - ocrWords: OcrWord[], - columnHeight: number - ): Array<{ word: string; confidence: number; y: number }> => { - const entries = ocrWords - .map((item) => { - const word = item.text.replace(/[^a-zA-Z]/g, '').toLowerCase(); - const yCenter = item.y + item.h / 2; - return { - word, - confidence: item.confidence, - y: yCenter, - }; - }) - .filter((item) => item.word.length >= 3 && item.word.length <= 8) - .sort((a, b) => a.y - b.y); - - if (entries.length === 0) return []; - - const rowTolerance = Math.max(10, Math.round((columnHeight / 6) * 0.35)); - const groups: Array<{ - yMean: number; - items: Array<{ word: string; confidence: number; y: number }>; - }> = []; - - for (const entry of entries) { - const last = groups[groups.length - 1]; - if (!last || Math.abs(entry.y - last.yMean) > rowTolerance) { - groups.push({ yMean: entry.y, items: [entry] }); - continue; - } - last.items.push(entry); - last.yMean = last.items.reduce((sum, item) => sum + item.y, 0) / last.items.length; - } - - return groups.slice(0, 6).map((group) => { - const best = [...group.items].sort((a, b) => b.confidence - a.confidence)[0]; - return { - word: best.word, - confidence: best.confidence, - y: group.yMean, - }; - }); - }; - - const applyColumnRows = ( - rowWords: Array<{ word: string; confidence: number; y: number }>, - startIndex: number, - endIndex: number, - columnHeight: number - ) => { - if (rowWords.length === 0) return; - - const slotCount = endIndex - startIndex + 1; - const maxShift = Math.max(0, slotCount - rowWords.length); - const rowHeight = columnHeight / slotCount; - const estimatedShift = Math.max( - 0, - Math.min(maxShift, Math.round(rowWords[0].y / rowHeight - 0.5)) - ); - - const topAlignedDense = rowWords.length >= 5 && rowWords[0].y <= columnHeight / 3; - let bestShift = topAlignedDense ? 0 : estimatedShift; - let bestScore = Number.NEGATIVE_INFINITY; - - if (!topAlignedDense) { - for (let shift = 0; shift <= maxShift; shift++) { - let score = -Math.abs(shift - estimatedShift) * 0.25; - rowWords.forEach((entry, rowIdx) => { - const index = startIndex + shift + rowIdx; - const existing = indexedWords.get(index); - if (!existing) return; - const normalized = entry.word.toLowerCase(); - const expectedWord = applyBip39Wordlist - ? correctToBip39(normalized).word - : applySlip39Wordlist - ? correctToSlip39(normalized).word - : normalized; - if (existing.word === expectedWord) { - score += 2; - } else if (levenshteinDistance(existing.word, expectedWord) <= 1) { - score += 1; - } - }); - if (score > bestScore) { - bestScore = score; - bestShift = shift; - } - } - } - - rowWords.forEach((entry, rowIdx) => { - const index = startIndex + bestShift + rowIdx; - if (index > endIndex) return; - upsertCandidateByConfidence(index, entry.word, entry.confidence, 'column-row'); - }); - }; - for (let i = 0; i < sceneCandidates.length; i++) { const scene = sceneCandidates[i]; const sceneResult = await runOcrOnRegion(scene, { @@ -1692,6 +1801,10 @@ function CameraPanel() { ocrParams: MNEMONIC_OCR_PARAMS, layoutHint: 'mnemonic', expectedWordCount, + deviceTestSetId: options?.deviceTestSetId, + // BIP39 and SLIP39 are distinct wordlists — tell the Paddle side which + // one this flow uses so variant scoring never mixes them. + wordlistHint: applyBip39Wordlist ? 'bip39' : 'slip39', }); if (!sceneResult) continue; @@ -1699,9 +1812,13 @@ function CameraPanel() { bestSceneResult = sceneResult; } - aggregatedRawText += `${aggregatedRawText ? '\n' : ''}[SCENE ${i + 1}] ${scene.roi.x},${scene.roi.y},${scene.roi.width},${scene.roi.height}\n${sceneResult.rawText}`; + // Rec output often glues a word to the next column's index ("gym13. stove"); + // the index patterns need whitespace/boundaries there, so re-split + // letter→digit seams before parsing. BIP39/SLIP39 words never contain digits. + const normalizedRawText = sceneResult.rawText.replace(/([a-zA-Z])(\d)/g, '$1 $2'); + aggregatedRawText += `${aggregatedRawText ? '\n' : ''}[SCENE ${i + 1}] ${scene.roi.x},${scene.roi.y},${scene.roi.width},${scene.roi.height}\n${normalizedRawText}`; const confidenceMap = buildWordConfidenceMap(sceneResult.words); - extractIndexedFromText(sceneResult.rawText, sceneResult.confidence, confidenceMap); + extractIndexedFromText(normalizedRawText, sceneResult.confidence, confidenceMap); extractIndexedFromTokenPairs(sceneResult.words, sceneResult.confidence, confidenceMap); if (indexedWords.size === expectedWordCount) { @@ -1728,7 +1845,7 @@ function CameraPanel() { } if (orderedWords.length >= expectedWordCount) { for (let i = 0; i < expectedWordCount; i++) { - upsertCandidateByConfidence(i + 1, orderedWords[i], bestSceneResult.confidence, 'default'); + upsertCandidateByConfidence(i + 1, orderedWords[i], bestSceneResult.confidence); } console.warn( `[CameraPanel] Mnemonic OCR used sequential fallback for ${expectedWordCount} words (indices unreadable).` @@ -1736,69 +1853,6 @@ function CameraPanel() { } } - // If direct indexed parsing is incomplete, run per-column OCR on the same frame - // and fill missing slots by row order. This is strategy-level fallback, not image filtering. - if ( - indexedWords.size < expectedWordCount - && OCR_BACKEND !== 'paddleocr_en' - && expectedWordCount === 12 - ) { - const video = videoRef.current; - const sharedFrameCanvas = options?.frameCanvas - ?? (video && video.readyState >= 2 ? captureRotatedFrameCanvas(video) : undefined); - - if (sharedFrameCanvas) { - const centerGap = 24; - const leftWidth = Math.floor((sceneConfig.roi.width - centerGap) / 2); - const rightWidth = sceneConfig.roi.width - centerGap - leftWidth; - const leftScene: OcrSceneConfig = { - ...sceneConfig, - roi: { - x: sceneConfig.roi.x, - y: sceneConfig.roi.y, - width: leftWidth, - height: sceneConfig.roi.height, - }, - }; - const rightScene: OcrSceneConfig = { - ...sceneConfig, - roi: { - x: sceneConfig.roi.x + leftWidth + centerGap, - y: sceneConfig.roi.y, - width: rightWidth, - height: sceneConfig.roi.height, - }, - }; - - const leftResult = await runOcrOnRegion(leftScene, { - frameCanvas: sharedFrameCanvas, - ocrParams: MNEMONIC_COLUMN_OCR_PARAMS, - }); - const rightResult = await runOcrOnRegion(rightScene, { - frameCanvas: sharedFrameCanvas, - ocrParams: MNEMONIC_COLUMN_OCR_PARAMS, - }); - - if (leftResult) { - aggregatedRawText += `\n[LEFT]\n${leftResult.rawText}`; - const leftConfidenceMap = buildWordConfidenceMap(leftResult.words); - extractIndexedFromText(leftResult.rawText, leftResult.confidence, leftConfidenceMap); - extractIndexedFromTokenPairs(leftResult.words, leftResult.confidence, leftConfidenceMap); - const leftRows = extractColumnWordsByBoxes(leftResult.words, leftScene.roi.height); - applyColumnRows(leftRows, 1, 6, leftScene.roi.height); - } - - if (rightResult) { - aggregatedRawText += `\n[RIGHT]\n${rightResult.rawText}`; - const rightConfidenceMap = buildWordConfidenceMap(rightResult.words); - extractIndexedFromText(rightResult.rawText, rightResult.confidence, rightConfidenceMap); - extractIndexedFromTokenPairs(rightResult.words, rightResult.confidence, rightConfidenceMap); - const rightRows = extractColumnWordsByBoxes(rightResult.words, rightScene.roi.height); - applyColumnRows(rightRows, 7, 12, rightScene.roi.height); - } - } - } - const mnemonicWords = Array.from(indexedWords.values()); // Sort by original index number mnemonicWords.sort((a, b) => a.index - b.index); @@ -1878,10 +1932,11 @@ function CameraPanel() { setPreOcrImageUrl(null); setNumberImageUrl(null); setNumberPreOcrImageUrl(null); + latestMnemonicPreOcrImageRef.current = null; try { await waitForVideoToSettle(video); - const mnemonicSceneConfig = getCurrentMnemonicSceneConfig(); + const mnemonicSceneConfig = getMnemonicSceneConfig(); if (RAW_MNEMONIC_OCR_DEBUG_ONLY) { const rawResult = await runOcrOnRegion(mnemonicSceneConfig ?? MNEMONIC_SCENE, { @@ -1911,18 +1966,21 @@ function CameraPanel() { missingIndices: number[]; } | null = null; - // Try OCR with retries + // Try OCR with retries; each attempt captures a fresh frame, so word-level + // votes across attempts cancel out frame-independent camera noise. + const voteAccumulator = createMnemonicVoteAccumulator(EXPECTED_MNEMONIC_COUNT, true); for (let attempt = 1; attempt <= MAX_OCR_RETRIES; attempt++) { console.log(`OCR attempt ${attempt}/${MAX_OCR_RETRIES}...`); const result = await runSingleOcr({ sceneConfig: mnemonicSceneConfig }); - + if (!result) { console.warn('OCR attempt failed'); continue; } console.log(`Attempt ${attempt}: Found ${result.mnemonicWords.length} words, confidence: ${result.confidence.toFixed(0)}%`); + voteAccumulator.add(result.mnemonicWords); // Keep track of best result: prefer complete index sequence, then BIP39 validity, then word count. const resultScore = (result.hasCompleteSequence ? 1000 : 0) @@ -1944,6 +2002,20 @@ function CameraPanel() { break; } + // Cross-frame consensus: majority vote per word slot over all attempts so far. + const consensus = voteAccumulator.consensus(); + if (consensus && consensus.hasCompleteSequence && consensus.bip39Valid) { + console.log('Success! Cross-frame consensus mnemonic passed BIP39 checksum.'); + bestResult = { + ...result, + mnemonicWords: consensus.mnemonicWords, + bip39Valid: true, + hasCompleteSequence: true, + missingIndices: [], + }; + break; + } + // Wait before retry if (attempt < MAX_OCR_RETRIES) { console.log(`Retrying in ${OCR_RETRY_DELAY}ms...`); @@ -2226,6 +2298,11 @@ function CameraPanel() { // Set up IPC listener for OCR-input image capture (crop + scale only). const unsubPreOcr = window.electronAPI?.onCapturePreOcrRequest?.(() => { + if (latestMnemonicPreOcrImageRef.current) { + window.electronAPI?.sendCapturePreOcrResponse?.(latestMnemonicPreOcrImageRef.current); + return; + } + const video = videoRef.current; const canvas = canvasRef.current; const ocrCanvas = ocrCanvasRef.current; @@ -2233,13 +2310,14 @@ function CameraPanel() { window.electronAPI?.sendCapturePreOcrResponse?.(null); return; } - const built = buildPreOcrCanvasFromVideo(video, canvas, ocrCanvas, MNEMONIC_SCENE); + const sceneConfig = getMnemonicSceneConfig() ?? MNEMONIC_SCENE; + const built = buildPreOcrCanvasFromVideo(video, canvas, ocrCanvas, sceneConfig); if (!built) { window.electronAPI?.sendCapturePreOcrResponse?.(null); return; } const dataUrl = OCR_BACKEND === 'paddleocr_en' - ? buildPaddleInputDataUrl(ocrCanvas, MNEMONIC_SCENE.useNearestNeighbor) + ? buildPaddleInputDataUrl(built.cropCanvas) : ocrCanvas.toDataURL('image/jpeg', OCR_JPEG_QUALITY); window.electronAPI?.sendCapturePreOcrResponse?.(dataUrl); }); @@ -2362,12 +2440,30 @@ function CameraPanel() { const triggerOptions = ((event as CustomEvent).detail ?? {}) as OcrTriggerOptions; const expectedWordCount = Math.max( 12, - Math.min(24, Math.floor(triggerOptions.expectedWordCount ?? EXPECTED_MNEMONIC_COUNT)) + Math.min(33, Math.floor(triggerOptions.expectedWordCount ?? EXPECTED_MNEMONIC_COUNT)) ); const mergeWithStored = !!triggerOptions.mergeWithStored; const allowPartial = !!triggerOptions.allowPartial; const requireBip39 = triggerOptions.requireBip39 ?? true; - const triggerSceneConfig = withOcrSceneDefaults(triggerOptions.sceneConfig, MNEMONIC_SCENE); + // requireBip39 only means "the full checksum must validate" — it is false + // for split captures (24-word part1) whose half-page can never validate. + // The wordlist follows the share size instead: SLIP39 shares are 20 or 33 + // words, everything else is BIP39. Deriving the wordlist from requireBip39 + // used to snap valid BIP39 part1 words onto the SLIP39 list (skull→skunk). + const useBip39Wordlist = expectedWordCount !== 20 && expectedWordCount !== 33; + const deviceTestSetId = normalizePreferredDeviceTestSet( + triggerOptions.deviceTestSetId ?? getStoredDeviceTestSet() + ); + const deviceSceneResolution = + resolveDeviceMnemonicScene(deviceTestSetId, expectedWordCount, { allowPartial }); + const defaultMnemonicSceneConfig = + withOcrSceneDefaults(deviceSceneResolution.scene, MNEMONIC_SCENE) ?? MNEMONIC_SCENE; + const triggerSceneConfig = + withOcrSceneDefaults(triggerOptions.sceneConfig, defaultMnemonicSceneConfig) + ?? defaultMnemonicSceneConfig; + // A sequence-provided scene is authored for the exact step, so treat it + // as calibrated too. + const sceneCalibrated = !!triggerOptions.sceneConfig || deviceSceneResolution.layoutSpecific; console.log( `[CameraPanel] External OCR trigger received (expected=${expectedWordCount}, merge=${mergeWithStored}, allowPartial=${allowPartial}, requireBip39=${requireBip39})` @@ -2378,6 +2474,7 @@ function CameraPanel() { setPreOcrImageUrl(null); setNumberImageUrl(null); setNumberPreOcrImageUrl(null); + latestMnemonicPreOcrImageRef.current = null; // Start-of-capture cleanup: // - 12/18 non-merge capture should drop stale words from previous runs. @@ -2404,8 +2501,9 @@ function CameraPanel() { await waitForVideoToSettle(video); if (RAW_MNEMONIC_OCR_DEBUG_ONLY) { - const rawResult = await runOcrOnRegion(MNEMONIC_SCENE, { + const rawResult = await runOcrOnRegion(triggerSceneConfig ?? MNEMONIC_SCENE, { ocrParams: MNEMONIC_OCR_PARAMS, + layoutHint: 'mnemonic', }); if (!rawResult) { throw new Error('Raw OCR capture failed'); @@ -2432,6 +2530,8 @@ function CameraPanel() { return; } + // Word-level votes across retry frames cancel out frame-independent camera noise. + const voteAccumulator = createMnemonicVoteAccumulator(expectedWordCount, useBip39Wordlist); for (let attempt = 1; attempt <= MAX_OCR_RETRIES; attempt++) { console.log(`[CameraPanel] OCR attempt ${attempt}/${MAX_OCR_RETRIES}...`); const result = await runSingleOcr({ @@ -2444,11 +2544,15 @@ function CameraPanel() { } }, sceneConfig: triggerSceneConfig, + sceneCalibrated, expectedWordCount, - applyBip39Wordlist: requireBip39, + applyBip39Wordlist: useBip39Wordlist, + deviceTestSetId, }); if (!result) continue; + voteAccumulator.add(result.mnemonicWords); + const resultScore = (result.hasCompleteSequence ? 1000 : 0) + (result.bip39Valid ? 500 : 0) + result.mnemonicWords.length; @@ -2472,11 +2576,44 @@ function CameraPanel() { break; } + // Cross-frame consensus: majority vote per word slot over all attempts so far. + const consensus = voteAccumulator.consensus(); + if (consensus && consensus.hasCompleteSequence && (!requireBip39 || consensus.bip39Valid)) { + console.log('[CameraPanel] Cross-frame consensus mnemonic is capture-ready.'); + bestWords = consensus.mnemonicWords; + bestConfidence = result.confidence; + bestImageDataUrl = result.imageDataUrl; + bestPreOcrImageDataUrl = result.preOcrImageDataUrl; + bestBip39Valid = consensus.bip39Valid; + bestHasCompleteSequence = true; + bestMissingIndices = []; + break; + } + if (attempt < MAX_OCR_RETRIES) { await new Promise((resolve) => setTimeout(resolve, OCR_RETRY_DELAY)); } } + // Scrolled part-captures can parse a phantom index from a viewport- + // clipped row (e.g. "1. ens" on a page showing only 3-12/15-24). Real + // indices come in contiguous column runs, so an index with neither + // neighbor present is noise — drop it before it can overwrite a correct + // stored word during the merge. + if (mergeWithStored && bestWords.length >= 12) { + const indices = new Set(bestWords.map((w) => w.index)); + const isolated = bestWords.filter( + (w) => !indices.has(w.index - 1) && !indices.has(w.index + 1) + ); + if (isolated.length > 0 && isolated.length < bestWords.length) { + console.log( + `[CameraPanel] Dropping isolated phantom indices: ${isolated.map((w) => `${w.index}.${w.word}`).join(' ')}` + ); + const isolatedIndices = new Set(isolated.map((w) => w.index)); + bestWords = bestWords.filter((w) => !isolatedIndices.has(w.index)); + } + } + const indexedToMap = (items: MnemonicWord[]): Map => { const map = new Map(); items.forEach((item) => { @@ -2500,6 +2637,11 @@ function CameraPanel() { return missing; }; + console.log( + `[CameraPanel] Capture consensus (expected=${expectedWordCount}, allowPartial=${allowPartial}): ` + + bestWords.map((w) => `${w.index}.${w.word}${w.original ? `(<-${w.original})` : ''}`).join(' ') + ); + let effectiveMissingIndices = [...bestMissingIndices]; let finalWordMap = indexedToMap(bestWords); if (mergeWithStored && bestWords.length > 0) { @@ -2552,6 +2694,9 @@ function CameraPanel() { } catch { finalBip39Valid = false; } + console.log( + `[CameraPanel] Final merged BIP39 validation: ${finalBip39Valid ? 'VALID' : 'INVALID'} - "${words.join(' ')}"` + ); } // Update local CameraPanel display @@ -2672,8 +2817,16 @@ function CameraPanel() { const handleTriggerVerifyOcr = async (event: Event) => { const triggerOptions = ((event as CustomEvent).detail ?? {}) as VerifyOcrTriggerOptions; - const numberSceneConfig = withOcrSceneDefaults(triggerOptions.numberSceneConfig, VERIFY_NUMBER_SCENE); - const optionsSceneConfig = withOcrSceneDefaults(triggerOptions.optionsSceneConfig, VERIFY_OPTIONS_SCENE); + const deviceTestSetId = normalizePreferredDeviceTestSet( + triggerOptions.deviceTestSetId ?? getStoredDeviceTestSet() + ); + const deviceVerifySceneConfigs = getVerifySceneConfigs(deviceTestSetId); + const numberSceneConfig = + withOcrSceneDefaults(triggerOptions.numberSceneConfig, VERIFY_NUMBER_SCENE) + ?? deviceVerifySceneConfigs.numberSceneConfig; + const optionsSceneConfig = + withOcrSceneDefaults(triggerOptions.optionsSceneConfig, VERIFY_OPTIONS_SCENE) + ?? deviceVerifySceneConfigs.optionsSceneConfig; console.log('[CameraPanel] Verification OCR trigger received'); setIsOcrProcessing(true); setFullFrameImageUrl(null); @@ -2708,7 +2861,7 @@ function CameraPanel() { let selectedAttemptScore = Number.NEGATIVE_INFINITY; const verifyMaxIndex = Math.max( 12, - Math.min(24, Math.floor(storedMnemonic?.words?.length ?? 12)) + Math.min(33, Math.floor(storedMnemonic?.words?.length ?? 12)) ); const hasStoredMnemonicWords = !!storedMnemonic && storedMnemonic.words.length > 0; const resolveTensAmbiguousFallback = (index: number): number => { @@ -2733,6 +2886,7 @@ function CameraPanel() { frameCanvas, maxIndex: verifyMaxIndex, sceneConfig: numberSceneConfig, + deviceTestSetId, onImageForOcr: (dataUrl) => { if (!verifyNumberSaved && window.electronAPI?.saveCaptureToDownloads) { verifyNumberSaved = true; @@ -2762,6 +2916,8 @@ function CameraPanel() { frameCanvas, targetWord, sceneConfig: optionsSceneConfig, + deviceTestSetId, + wordlistHint: inferWordlistHint(storedMnemonic?.words), onImageForOcr: (dataUrl) => { if (!verifyOptionsSaved && window.electronAPI?.saveCaptureToDownloads) { verifyOptionsSaved = true; @@ -2998,9 +3154,13 @@ function CameraPanel() { // Manually trigger verification-page OCR debug flow (single-frame dual ROI). const handleVerifyDebugOcr = () => { + const deviceTestSetId = getStoredDeviceTestSet(); window.dispatchEvent( new CustomEvent('qa-auto-hw:trigger-verify-ocr', { - detail: getCurrentVerifySceneConfigs(), + detail: { + ...getVerifySceneConfigs(deviceTestSetId), + deviceTestSetId, + }, }) ); }; diff --git a/src/components/ControlPanel.tsx b/src/components/ControlPanel.tsx index 7ade623..8e4fc86 100644 --- a/src/components/ControlPanel.tsx +++ b/src/components/ControlPanel.tsx @@ -679,6 +679,10 @@ function ControlPanel() { const step = steps[i]; setState((prev) => ({ ...prev, autoProgress: i + 1 })); + if ((step.delayBefore ?? 0) > 0) { + await delay(step.delayBefore ?? 0); + } + if (step.moveOnly) { await send(`X${step.x}Y${step.y}`); addLog('自动', `${step.label} - 移动到 (${step.x},${step.y})`); @@ -700,6 +704,7 @@ function ControlPanel() { window.dispatchEvent( new CustomEvent('qa-auto-hw:trigger-verify-ocr', { detail: { + deviceTestSetId, numberSceneConfig: verifyScenes?.number, optionsSceneConfig: verifyScenes?.options, }, @@ -776,7 +781,12 @@ function ControlPanel() { }; window.addEventListener('qa-auto-hw:ocr-result', handler); window.dispatchEvent( - new CustomEvent('qa-auto-hw:trigger-ocr', { detail: ocrCaptureConfig }) + new CustomEvent('qa-auto-hw:trigger-ocr', { + detail: { + ...ocrCaptureConfig, + deviceTestSetId, + }, + }) ); }), new Promise((resolve) => diff --git a/src/ocr/deviceScenes.ts b/src/ocr/deviceScenes.ts index 87c52d2..9806f40 100644 --- a/src/ocr/deviceScenes.ts +++ b/src/ocr/deviceScenes.ts @@ -27,6 +27,50 @@ export type DeviceOcrScenes = { }; }; +export const PRO_OCR_SCENES = { + createWallet: { + // 12-word grid only (rows at y≈725-1227): excludes status bar and Chinese + // header so OCR resolution goes entirely to the numbered words. + mnemonic12: { + roi: { x: 225, y: 690, width: 665, height: 580 }, + scale: 5, + useNearestNeighbor: true, + }, + // 18-word page: no big title, instruction line ends ~y565, 9 grid rows at + // y≈610-1370 (the legacy ROI bottom at 1360 clipped row 9). + mnemonic18: { + roi: { x: 225, y: 580, width: 665, height: 830 }, + scale: 5, + useNearestNeighbor: true, + }, + // 20/24-word lists are a scrolled 2-column window. Part1 (unscrolled): + // header above, 8 rows at y≈729-1401, cyan 继续 button from y≈1419 — the + // ROI must stop above it. Part2 (scrolled): full rows at y≈565-1322 with a + // half-clipped row near the status bar; span the whole list viewport to + // absorb scroll-position variance between runs. + mnemonic24Part1: { + roi: { x: 225, y: 695, width: 665, height: 715 }, + scale: 5, + useNearestNeighbor: true, + }, + mnemonic24Part2: { + roi: { x: 225, y: 520, width: 665, height: 890 }, + scale: 5, + useNearestNeighbor: true, + }, + }, + verifyWallet: { + // "单词 #N" title sits at y≈545-615; the old default ROI (y 480-610) was + // almost entirely above it. Keep the title in the upper part so the tight + // first-line fallback crop (y-20, 72% height) still contains the digits. + number: { + roi: { x: 160, y: 530, width: 520, height: 170 }, + scale: 5, + useNearestNeighbor: true, + }, + }, +} as const satisfies DeviceOcrScenes; + export const PRO2_OCR_SCENES = { createWallet: { mnemonic12: { @@ -50,6 +94,6 @@ export const PRO2_OCR_SCENES = { } as const satisfies DeviceOcrScenes; export const DEVICE_OCR_SCENES: Record<'pro' | 'pro2', DeviceOcrScenes> = { - pro: {}, + pro: PRO_OCR_SCENES, pro2: PRO2_OCR_SCENES, }; diff --git a/src/vite-env.d.ts b/src/vite-env.d.ts index 02408ad..5aa1fa9 100644 --- a/src/vite-env.d.ts +++ b/src/vite-env.d.ts @@ -73,7 +73,7 @@ interface VerifyOcrPayload { interface PaddleOcrEnPayload { text: string; confidence: number; - backend: 'en_PP-OCRv5_mobile_rec'; + backend: 'PP-OCRv6_medium_rec_onnx'; elapsedMs: number; } @@ -94,6 +94,12 @@ interface ResolvedSequenceStepPayload { mergeWithStored?: boolean; allowPartial?: boolean; requireBip39?: boolean; + sceneConfig?: { + roi: { x: number; y: number; width: number; height: number }; + scale?: number; + useNearestNeighbor?: boolean; + }; + deviceTestSetId?: 'pro' | 'pro2'; }; ocrVerify?: { options: { x: number; y: number; depth: number }[]; @@ -154,7 +160,8 @@ interface Window { imageDataUrl: string, layoutHint?: 'mnemonic' | 'verify-options' | 'verify-number' | 'generic', expectedWordCount?: number, - recVariantCount?: number + recVariantCount?: number, + wordlistHint?: 'bip39' | 'slip39' ) => Promise; onMcpServerReady: (callback: (info: { port: number }) => void) => void; // MCP Logs