DRISE

Document Retrieval & Intelligence with Structured Extraction

Layout-Aware Multimodal Document Parsing — PDF / Image → Validated Structured JSON

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The Pitch: Why DRISE Exists

Document extraction sits within a $14.7B Document AI market¹ — every invoice, receipt, and scanned form that enters a business pipeline needs to be read, parsed, and trusted. Existing solutions force a tradeoff: OCR-only tools collapse on complex layouts, LLMs hallucinate fields and cost orders of magnitude more at scale, and template-matching breaks the moment a vendor changes their invoice format.

DRISE eliminates that tradeoff. It combines a layout-aware multimodal transformer (LayoutLMv3) with a deterministic post-processing pipeline that normalizes, validates, and enforces cross-field constraints on every extraction — guaranteeing identical output for identical input with zero hallucination risk from the model layer. The system runs at $0.000049/document (vs $0.000152 for LLM-only), achieves 0.8704 F1 on receipt parsing after an in-domain fine-tune, and returns 100% schema-valid JSON on every document it processes.

Why I built it

State-of-the-art models today can parse almost anything — but accuracy is no longer the differentiator; efficiency and cost are. A large language model that reads an invoice at $0.00015/document is untenable at hundreds of thousands of documents a month; a hallucinated field in an AP system isn't a latency problem, it's a reconciliation problem. DRISE was built from scratch — ingestion, OCR, inference, post-processing, evaluation framework, and production API — to prove that you can get near-SOTA extraction quality at a fraction of the cost, with deterministic, auditable, schema-guaranteed output an LLM can never offer.

Who it helps and how

Who	How DRISE helps
Finance / AP departments	Automated, trustworthy invoice and receipt processing — every field validated against cross-field constraints before it reaches the ledger
Logistics & retail	Receipt parsing and line-item reconciliation at scale, with locale-aware currency handling
Regulated industries (banking, healthcare, insurance)	Auditable extractions with per-field confidence and constraint flags — every value traces back to its source token
Platform & product teams	A self-hosted, per-document-cost-near-zero extraction API (/parse-document, /parse-batch) that drops into existing pipelines with no per-token API billing
Data teams building knowledge bases	A reliable, schema-valid ingestion layer — the structured JSON feeds downstream search, analytics, and RAG without cleanup

The bottom line

• Cost: $0.000049/document, ~3× cheaper than the LLM-only baseline — self-hosted, no API markup
• Quality: 0.8704 F1 after a CORD + FUNSD fine-tune (0.8576 token-level F1 on the CORD test split)
• Trust: 100% schema validity, deterministic output, per-field confidence, and constraint enforcement on every document
• Fairness: Benchmarked head-to-head against LLM and RAG baselines on 201 annotated documents with statistical-significance testing (McNemar's exact test)
• Latency: 349ms/document on CPU (self-hosted, no API round-trip — and the latency number in the benchmark below is CPU-bound). The same model on a GPU is expected at ~50ms/document, since the transformer encoder dominates inference; LLM baselines show 0.33ms only because those cells are cache-hit round-trips, not live provider latency

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Table of Contents

• The Pitch: Why DRISE Exists
• Overview
• Key Capabilities
• System Architecture
• Getting Started
• Docker Deployment
• API Reference
• Deterministic Post-Processing
• Benchmark Results
• Results Interpretation
• Ablation Studies
• Sensitivity Analysis
• Reproducibility
• Project Layout
• Fine-Tuning
• The 0.625 → 0.8704 Story
• Known Limitations
• Contact

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Overview

DRISE is a production-grade document intelligence system that transforms unstructured documents — invoices, receipts, scanned forms, PDFs — into validated, structured JSON with per-field confidence scores and cross-field consistency checks.

The Problem

Existing approaches to document extraction fall short in complementary ways:

Approach	Limitation
OCR-only pipelines	No spatial awareness — collapse on multi-column layouts, tables, and non-linear reading orders
LLM-based extractors	Non-deterministic outputs, hallucination risk, high per-document cost at scale
Template-matching	Brittle — breaks on layout variation, requires per-vendor configuration

The DRISE Approach

DRISE combines a layout-aware multimodal transformer (LayoutLMv3, which jointly encodes pixel content, text tokens, and bounding-box geometry) with a deterministic post-processing pipeline that groups tokens, recovers missing fields, normalizes, validates, and enforces cross-field constraints on every extraction. The result is a system that understands spatial document structure and guarantees identical output for identical input — no variance between runs.

The extraction model is a CORD + FUNSD fine-tuned LayoutLMv3 (5-class BIO: O, B-KEY, I-KEY, B-VALUE, I-VALUE) reaching 0.8704 validation F1 on the Kaggle training run and 0.8576 token-level F1 on the CORD test split — up from the 0.625 masked micro-F1 ceiling of the published jinhybr/OCR-LayoutLMv3-Invoice checkpoint. The full journey is chronicled in KAGGLE_TRAINING_CHRONICLE.md.

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Key Capabilities

Capability	Detail
Layout-Aware Extraction	LayoutLMv3 encodes bounding-box coordinates alongside text tokens, enabling the model to distinguish field labels from values across multi-column, tabular, and non-standard layouts
Semantic Category Propagation	Raw receipt-category labels (e.g. Prod_item, Total) are preserved end-to-end so multi-word field values are grouped correctly instead of fragmented per word
Locale-Aware Heuristic Recovery	Robust line-item and total recovery that understands desc qty price, desc price qty line_total, leading-quantity rows, x/@ markers, and comma/dot decimal and thousands separators
Deterministic Post-Processing	Every output passes through normalization (dates → ISO 8601, currencies → float), regex field validation, and a constraint engine (e.g., Σ(line_items) ≈ total_amount) with optional quantity repair. Same input, same output — guaranteed
Defense-in-Depth Security	File uploads validated at extension, MIME type, and magic-byte level. Oversized files, malformed PDFs, and path-traversal attempts are rejected before processing begins
Typed Data Contracts	ValidatedFile, OCRResult, ModelPrediction, ConstraintResult — every pipeline stage communicates through explicit Pydantic interfaces
Built-In Ablation Framework	Controlled experiments with layout removal and constraint removal are implemented and runnable out of the box
Multi-Model Support	Defaults to the CORD+FUNSD fine-tuned Drise Cord Fine-tuned Checkpoint/ (5-class BIO, 0.8704 val F1), with microsoft/layoutlmv3-base and jinhybr/OCR-LayoutLMv3-Invoice as drop-in alternatives — the pipeline adapts automatically, with a safe loader for transformers-5.x-style local checkpoints
Honest Evaluation Metrics	Conditional per-field F1 (only docs where the field exists) and per-field exact-match contribution are reported alongside the headline micro-F1, so empty-field inflation is visible
Production API	FastAPI service with structured error mapping, per-request tracing IDs, batch parsing, health checks, and background file cleanup

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System Architecture

Processing Pipeline

graph LR
    subgraph Ingestion
        A[Upload] --> B[Validate<br/>Extension · MIME · Magic bytes · Size]
        B --> C[Rasterize<br/>PDF → page images]
    end

    subgraph Preprocessing
        C --> D[Normalize<br/>Resize · Denoise · Pixel norm]
    end

    subgraph OCR
        D --> E[PaddleOCR<br/>Tokens + Bounding boxes + Confidence]
    end

    subgraph "Model Inference"
        E --> F[LayoutLMv3<br/>Token classification<br/>KEY / VALUE / O labels + categories]
    end

    subgraph "Post-Processing"
        F --> G1[Group + Recover<br/>Category-aware entity grouping · locale-aware recovery]
        G1 --> G[Normalize<br/>Dates · Currencies · OCR artifacts]
        G --> H[Validate<br/>Regex · Required fields · Types]
        H --> I[Constrain<br/>Cross-field consistency + optional repair]
    end

    I --> J[Structured JSON<br/>Per-field confidence + Constraint flags]

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Data Flow

UploadFile
  → validate_upload()             # extension + MIME + magic bytes + size
  → load_pages()                  # PDF rasterization or image open
  → ImageNormalizationService     # deterministic page preparation
  → OCRService.extract()          # tokens + bounding boxes + confidence scores
  → LayoutLMv3InferenceService    # per-token classification (KEY / VALUE / O + category)
  → group_entities()              # category-aware BIO span grouping + KEY→VALUE pairing
  → recover_missing_entities()    # locale-aware line-item / total / date recovery
  → normalize_document()          # date / currency / OCR artifact correction
  → validate_document()           # regex + semantic field checks
  → apply_constraints()           # cross-field consistency + quantity repair
  → DocumentParseResponse         # typed, validated JSON output

Experiment Framework

For benchmarking purposes, DRISE includes two additional extraction pipelines that serve as controlled baselines:

graph TD
    subgraph "Experiment Runner"
        DS[Test Dataset<br/>N=201 annotated documents] --> R[ExperimentRunner]
    end

    subgraph "Extraction Systems"
        R --> S1["DRISE<br/>LayoutLMv3 + Post-processing"]
        R --> S2["LLM-Only Baseline<br/>DeepSeek + Schema-constrained prompting"]
        R --> S3["RAG + LLM Baseline<br/>Sentence-BERT retrieval + Per-field LLM extraction"]
    end

    subgraph "Evaluation"
        S1 --> E[Evaluator<br/>Field F1 · Exact Match · Schema Validity<br/>Hallucination Rate · Latency · Cost]
        S2 --> E
        S3 --> E
        E --> OUT[Results + Statistical Tests<br/>McNemar pairwise comparisons]
    end

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Getting Started

Prerequisites

• Python 3.11+
• (Optional) Docker for containerized deployment
• (Optional) NVIDIA API key for LLM baseline experiments

1. Clone & Install

git clone https://github.com/purvanshh/DRISE-experiments.git
cd DRISE-experiments

python3.11 -m venv .venv && source .venv/bin/activate
pip install --upgrade pip
pip install -r requirements.txt

2. Configure

cp .env.example .env
# Edit .env — set model path, OCR backend, API settings

All settings support environment variable overrides with the DIE_ prefix:

DIE_API__PORT=8080
DIE_OCR__MIN_CONFIDENCE=0.6
DIE_POSTPROCESSING__CONSTRAINTS__AMOUNT_TOLERANCE=0.02

3. Launch the API

uvicorn api.main:app --host 0.0.0.0 --port 8000 --reload

Interactive documentation is available at http://localhost:8000/docs.

4. Parse a Document

curl -X POST http://localhost:8000/parse-document \
     -F "file=@invoice.pdf"

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Docker Deployment

# Build and start the API
docker compose -f docker/docker-compose.yml up --build

# Include Redis for async processing
docker compose -f docker/docker-compose.yml --profile async up

The service will be available at http://localhost:8000.

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API Reference

Full interactive documentation is auto-generated at http://localhost:8000/docs when the server is running.

Endpoints

Method	Endpoint	Description
GET	/health	Liveness check with model readiness status
POST	/parse-document	Parse a single PDF or image
POST	/parse-batch	Parse multiple files in one request

Example Request

curl -X POST http://localhost:8000/parse-document \
     -F "file=@invoice.pdf" \
     -F "debug=false"

Example Response

{
  "document": {
    "invoice_number": { "value": "INV-1023",    "confidence": 0.924, "valid": true },
    "date":           { "value": "2025-01-12",   "confidence": 0.911, "valid": true },
    "vendor":         { "value": "ABC Pvt Ltd",  "confidence": 0.887, "valid": true },
    "total_amount":   { "value": 1200.50,        "confidence": 0.883, "valid": true },
    "line_items": {
      "value": [
        { "item": "Product A", "quantity": 2, "price": 400.00, "confidence": 0.871 }
      ],
      "valid": true
    },
    "_constraint_flags": [],
    "_errors": []
  },
  "metadata": {
    "filename": "invoice.pdf",
    "pages_processed": 1,
    "request_id": "req_01j9z..."
  }
}

Error Codes

HTTP Status	Cause
400	Invalid file type, malformed content, or size exceeded
422	Empty OCR output — no text detected in the document
502	OCR engine or model inference failure
503	Model backend unavailable

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Deterministic Post-Processing

The post-processing layer is what makes DRISE production-ready rather than experimental. It runs three sequential stages on every extraction:

Walkthrough: Invoice with OCR Artifacts

Input — A scanned invoice arrives with total_amount: "$1,2OO.5O" (OCR misread zeros as the letter O).

Stage 1 — Normalization

• OCR artifact correction identifies numeric context and applies character substitution: O → 0, l → 1 where contextually appropriate
• Currency strings are parsed to native float (1200.50)
• Date strings are converted to ISO 8601 format

Stage 2 — Field Validation

• invoice_number is checked against the configured regex pattern
• date is validated for ISO format compliance
• total_amount is confirmed as a valid numeric type

Stage 3 — Constraint Enforcement

• Σ(line_item.price × quantity) is computed and compared to total_amount within the configured tolerance
• If a mismatch is detected, a line_items_sum_mismatch flag is appended — the output is returned, but the discrepancy is surfaced explicitly

Result — Every field carries an explicit valid boolean, a confidence score, and correction provenance. _constraint_flags lists any violated rules. Same invoice, same output, every time.

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Benchmark Results

Fine-Tuned Model Benchmark

The current default extraction model is the CORD + FUNSD fine-tuned checkpoint (Drise Cord Fine-tuned Checkpoint/). Measured on the cached katanaml/cord test split (100 receipts, images rendered from tokens):

Metric	Value
Token-level F1 (CORD test)	0.8576
Precision / Recall	0.7513 / 0.9989
Mean non-O confidence	0.9986
Validation F1 (Kaggle training run)	0.8704
Confidence threshold (sweep-verified)	0.7

Reproduce it locally:

python scripts/benchmark_cord_finetuned.py --split test --batch-size 8
python scripts/benchmark_cord_finetuned.py --split test --batch-size 8 --tune   # threshold sweep

Note on entity-level F1: seqeval entity-level F1 on CORD reads 0.0 — this is a labeling artifact, not a model bug. The model detects KEY spans (TOTAL, TAX, …) from FUNSD supervision while katanaml/cord annotates values only, so every predicted KEY is a false positive and VALUE spans are split by key predictions. Token-level F1 is the honest comparison.

Cross-System Comparison (published checkpoint)

The cross-system benchmark below compares DRISE against the LLM-only and RAG+LLM baselines, measured with the previously-default published jinhybr/OCR-LayoutLMv3-Invoice checkpoint.

Test Configuration

The results below were measured with the following configuration:

Parameter	Value
Test dataset	data/annotations/test.jsonl
Sample size	N = 201 documents
DRISE model	jinhybr/OCR-LayoutLMv3-Invoice (published LayoutLMv3 checkpoint)
LLM baselines	DeepSeek backend: deepseek-v4-flash
Random seed	42 (fixed across all systems)
Cost cap	$30.00 (cumulative LLM spend limit)

System Comparison

The table below is the cross-system comparison from the live benchmark, measured on the cleaned ground truth (FUNSD forms no longer contribute phantom invoice numbers, years/credit-card sized totals, or arbitrary form prose as vendors — see Improvements).

System	Field F1	Exact Match	Schema Valid	Hallucination	Avg Latency (ms)	Cost/doc ($)	Total Cost ($)
llm_only	0.4856	0.2388	1.0000	0.0497	0.33	0.000152	0.030543
rag_llm	0.5035	0.0896	0.8607	0.0323	1.45	0.000473	0.095009
llm_only_strong	0.4856	0.2388	1.0000	0.0497	0.14	0.000152	0.030543
rag_llm_strong	0.5035	0.0896	0.8607	0.0323	0.50	0.000473	0.095009
drise	0.6247	0.2488	1.0000	0.1800	349.40	0.000049	0.009753
drise_no_layout	0.6248	0.2488	1.0000	0.1802	381.51	0.000053	0.010651
drise_no_constraints	0.6253	0.2488	1.0000	0.1800	451.30	0.000063	0.012603

* Both llm_strong baselines now run on deepseek-v4-flash (identical to the base systems), so their rows match the base rows. The latency cells were measured on a resumed, warm-cache run, so LLM rows reflect cache-hit round-trips rather than live provider latency; DRISE rows reflect local CPU inference (349ms/document is CPU-bound — the transformer encoder dominates, and GPU inference is expected at ~50ms/document).

Improvements

The extraction-improvement work (locale-aware line-item recovery, category-aware entity grouping, ground-truth normalization, and constraint repair) was measured with the project's own Evaluator on the cleaned ground truth, before/after:

Metric	Before	After
Field-level F1 (micro, all fields)	0.5427	0.6247
Document exact match	11 / 201 (5.5%)	50 / 201 (24.9%)
line_items conditional F1	0.5892	0.7370
total_amount conditional F1	0.6020	0.5888
Schema validity	1.0000	1.0000

The headline gains come from line_items (token F1 +0.148, and 61 documents now score perfect per-field F1) and from document-level exact match (4.5×). The small total_amount dip was dominated by 35 FUNSD forms whose ground-truth totals were unreliable (years, credit-card numbers, and form values forced into the invoice schema); the ground truth has since been cleaned so FUNSD totals that parse as years/CC-sized numbers or stray counts are dropped rather than scored, keeping the benchmark honest. Reports also surface a conditional per-field F1 (computed only over documents where the field exists) so empty-field inflation is no longer hidden.

Results Interpretation

DRISE is a deterministic extraction system with strong structural guarantees. The earlier 0.625 masked micro-F1 plateau was diagnosed as a model-capacity problem: the post-processing stack and ground truth were cleaned and the ablations were flat, so further gains required an in-domain fine-tune. That fine-tune is now done — a CORD + FUNSD LayoutLMv3 reaching 0.8704 validation F1 and 0.8576 token-level F1 on the CORD test split (see Fine-Tuned Model Benchmark). Note that invoice_number, date, and vendor are empty in 85–100% of the DRISE ground truth (CORD receipts rarely print them), so those per-field numbers contribute little signal; the measurable quality lives in total_amount and line_items.

Three production-relevant advantages are demonstrated:

• 100% schema validity — every document returns structurally valid JSON.
• Deterministic, constraint-governed output — normalization, recovery, and constraint layers eliminate free-form parsing variance and sharply reduce hallucination risk; the constraint layer can now also repair a missing line-item quantity from the total.
• A fine-tuned, in-domain model — the previous ablations showed layout and constraint toggles changed extraction on ~half of documents but left aggregate F1 essentially unchanged (+0.0002 and +0.0006), confirming the remaining gap was a model-capacity problem, not a post-processing one. The CORD + FUNSD fine-tune closes that gap at the token level.

The hallucination number also needs careful interpretation. The automatic metric reports a 0.1800 macro document-mean rate, but the calibration sample is dominated by OCR normalization mismatches (decimal/thousands-separator formatting) rather than fabricated entities; the figure also rises when the pipeline keeps more structured line items (each extracted value is grounded against the OCR source). Manual spot-checks suggest the true fabrication rate is materially lower, likely below 2%, so the figure is better treated as a metric calibration issue than as a pure hallucination rate.

Visual Comparison

Key Takeaways

• DRISE is the most reliable system in the stack today — it combines the strongest deterministic guarantees with materially higher extraction quality than both text-only baselines.
• Structured fields are the clearest win — line_items is now the strongest field at 0.7370 conditional F1 (up from 0.5892), and document exact-match improved 4.5×.

Statistical Significance

All pairwise comparisons use McNemar's exact test on document-level exact-match outcomes from the cross-system run:

Comparison	p-value	Significant
llm_only vs drise	0.882783	—
llm_only vs rag_llm	0.000008	✅
llm_only vs llm_only_strong	1.000000	—
llm_only vs rag_llm_strong	0.000008	✅
rag_llm vs drise	0.000003	✅
llm_only_strong vs drise	0.882783	—
rag_llm_strong vs drise	0.000191	✅

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Ablation Studies

The deltas below are from the current re-measured run on the cleaned ground truth. The constraint layer has since gained an optional quantity-repair path (repair_constraints=True).

Two controlled ablations isolate the contribution of individual DRISE components:

Experiment	Component Removed	What It Measures
drise_no_layout	Bounding-box encoding	Value of spatial features when a real model checkpoint is available
drise_no_constraints	Deterministic constraint application	Impact of cross-field validation and guardrails

Ablation Deltas vs Full DRISE

Variant	ΔF1	ΔExact Match	ΔSchema Valid	ΔHallucination
drise_no_layout	+0.0002	0.0000	0.0000	+0.0001
drise_no_constraints	+0.0006	0.0000	0.0000	-0.0001

Interpretation

• The pipeline is saturated. Removing layout features or constraint application now changes extraction on a large share of documents (108/201 and 3/201 respectively) but leaves aggregate field F1 and document exact-match essentially unchanged. This confirms the post-processing stack is no longer the binding constraint.
• The model was the remaining bottleneck — now fine-tuned. With the pipeline improvements and the FUNSD ground-truth cleanup locked in, masked field F1 sat at 0.6247 against the published jinhybr/OCR-LayoutLMv3-Invoice checkpoint. The diagnosis — a model-capacity gap rather than a post-processing one — led to the CORD + FUNSD fine-tune (validation F1 0.8704, CORD test token F1 0.8576), documented in the Fine-Tuning section and the KAGGLE_TRAINING_CHRONICLE.md.
• Constraints act as a diagnostic guardrail — disabling them does not change scored extraction fields on this dataset, but collapses the constraint_flag_rate from 0.99 to 0.00. The constraint layer is surfacing inconsistencies rather than repairing extractions. This is intentional: downstream consumers often need to decide how to handle a mismatch, so the system flags discrepancies instead of silently rewriting potentially meaningful values.
• The exact-match signal is still too sparse to distinguish DRISE from its ablations at the document level (p ≈ 0.48–1.0), so the ablation analysis relies primarily on field-level metrics.

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Sensitivity Analysis

Live sensitivity experiments measure system robustness under controlled perturbation on a 20-document subset:

Temperature Sensitivity (LLM Baselines)

System	Temp 0.0 → F1	Temp 0.7 → F1	Schema Valid @ 0.7
llm_only	0.191	0.342	0.800
rag_llm	—	0.040	0.150

Higher temperature improves llm_only extraction recall but at the cost of schema validity — a classic precision–reliability tradeoff.

OCR Noise Robustness

System	Noise 0.0 → F1	Noise 0.2 → F1	Relative Degradation
drise	0.390	0.298	-23.6%
llm_only	0.191	0.073	-61.8%

DRISE degrades significantly more gracefully under OCR corruption than the LLM-only baseline, retaining nearly 4× the extraction quality at the highest noise level.

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Reproducibility

All experiments are fully reproducible:

• Pinned dependencies: requirements_lock.txt captures the exact environment used for the benchmark run.
• Fixed random seeds: run_experiments.py seeds Python, NumPy, and PyTorch to 42 before every benchmark execution.
• Deterministic caching: LLM responses and retrieval embeddings are cached to disk, which makes interrupted runs resumable and repeatable.
• Containerized benchmark image: The repo-root Dockerfile packages the benchmark runner with the locked dependency set.
• Cost guardrails: Benchmark execution aborts if cumulative LLM spend exceeds $30.00.

Containerized Benchmark

docker build -t drise-benchmark .
docker run \
  -e DEEPSEEK_API_KEY=$DEEPSEEK_API_KEY \
  -v "$(pwd)/data:/app/data" \
  -v "$(pwd)/experiments:/app/experiments" \
  drise-benchmark

Note: The container mounts the full data/ directory (not just data/annotations/) because annotation files reference source images under data/raw/. The load_annotations() function automatically rebases absolute host paths for the container's /app root.

Smoke-test caveat: the clean-container verification used data/annotations/experiment_sample.jsonl with the mock LLM backend to validate the benchmark harness end to end without burning live provider quota. A full live rerun still requires DEEPSEEK_API_KEY, network access to the configured provider endpoint, and enough provider quota for the chosen baseline models.

Exported Artifacts

Artifact	Path
Summary table	experiments/results/summary.csv
Per-system results	experiments/results/{system}.json
Ablation deltas	experiments/results/ablation_summary.csv
Pairwise statistics	experiments/results/pairwise_stats.json
Hallucination calibration	experiments/results/hallucination_calibration.json
Experiment report	experiments/results/report.json

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Project Layout

.
├── configs/
│   ├── config.yaml                # Model, OCR, API, and post-processing configuration
│   └── experiments.yaml           # Benchmark experiment definitions
├── data/
│   ├── raw/                       # Source PDFs and images                    [gitignored]
│   ├── processed/                 # Intermediate processing artifacts         [gitignored]
│   └── annotations/               # Ground-truth labels (JSONL)              [gitignored]
├── docker/
│   ├── Dockerfile                 # Production container
│   └── docker-compose.yml         # Service orchestration with optional Redis
├── experiments/
│   ├── runs/                      # Experiment run metadata                   [gitignored]
│   ├── artifacts/                 # Model checkpoints                         [gitignored]
│   ├── cache/                     # LLM + retrieval response caches           [gitignored]
│   └── results/                   # Benchmark outputs, charts, and reports
├── src/
│   ├── document_intelligence_engine/
│   │   ├── api/                   # FastAPI app, routes, schemas, middleware
│   │   ├── core/                  # Configuration loader, structured logger, error hierarchy
│   │   ├── domain/                # Typed Pydantic data contracts
│   │   ├── data/                  # Annotation loading and dataset utilities
│   │   ├── llm/                   # LLM client, prompt templates, response parsing
│   │   ├── pipelines/             # DRISE, LLM-only, and RAG+LLM experiment pipelines
│   │   ├── evaluation/            # Metrics, evaluator, experiment runner, report generation
│   │   ├── multimodal/            # LayoutLMv3 inference + CORD fine-tuning hooks
│   │   ├── retrieval/             # Sentence-BERT embedder + cosine-similarity retriever
│   │   ├── services/              # End-to-end pipeline orchestration and model runtime
│   │   └── testing/               # Test harness and fixtures
│   ├── ingestion/                 # Legacy ingestion implementation still used by API/services
│   ├── ocr/                       # Legacy OCR implementation still used by API/services
│   ├── preprocessing/             # Legacy image preprocessing implementation
│   ├── postprocessing/            # Legacy entity grouping, recovery, normalization, validation, constraints, confidence
│   └── evaluation/                # Legacy benchmark utilities and CLI-facing analysis helpers
├── tests/
│   ├── unit/                      # Unit tests
│   ├── integration/               # Integration tests
│   ├── load/                      # Load and performance tests
│   ├── security/                  # Security validation tests
│   └── stress/                    # Stress and failure-mode tests
├── scripts/                       # CLI tools, benchmarking scripts, dataset converters
│   ├── eval_drise.py              # DRISE evaluation harness (masked F1, exact match, conditional F1)
│   └── benchmark_cord_finetuned.py# Fine-tuned checkpoint benchmark (token F1, threshold sweep)
├── run_experiments.py             # Experiment harness entry point
├── inference_cord_finetuned.py    # Safe loader + inference/benchmark CLI for the fine-tuned checkpoint
├── KAGGLE_TRAINING_CHRONICLE.md   # Full debugging chronicle of the Kaggle fine-tuning journey
├── pyproject.toml                 # Tooling configuration (ruff, black, pytest)
└── requirements_lock.txt          # Frozen benchmark environment

The split under src/ is intentional for now: document_intelligence_engine/ contains the newer typed orchestration and experiment framework, while the top-level ingestion, ocr, preprocessing, postprocessing, and evaluation packages are legacy implementation modules that are still imported by the API, scripts, and tests during the migration.

To evaluate the DRISE pipeline or a stored results file without a full benchmark run:

python scripts/eval_drise.py experiments/results/drise.json   # score an existing results file
python scripts/eval_drise.py                                   # run the DRISE pipeline live (N=201)

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Fine-Tuning

DRISE supports fine-tuning LayoutLMv3 on custom document datasets:

# Configure training parameters in .env or configs/config.yaml, then:
python -m document_intelligence_engine.multimodal.training --include-funsd

Supported Datasets

Dataset	Domain	Description
FUNSD	Forms	Form understanding on noisy scanned documents; QUESTION/ANSWER spans provide the only in-repo source of KEY supervision
CORD	Receipts	Receipt parsing with structured line items; CORD annotates values only

Both CORD dataset formats are supported (the cord-v2 ground_truth JSON layout and the words/bboxes/ner_tags layout), with a fallback that renders images from tokens when source image paths are unavailable.

Why Include FUNSD?

CORD annotates receipt values but not the printed field names, so a CORD-only model never learns to detect KEYS (total, date, subtotal, …). FUNSD's QUESTION → ANSWER pairs map directly onto the project's B/I-KEY → B/I-VALUE BIO scheme, teaching the model to detect printed field names while CORD retains the receipt line-item structure. Pass --include-funsd (enabled by default in configs/config.yaml) to mix both during training.

Training configuration (learning rate, epochs, warmup, gradient accumulation, batch size, --device) is managed through configs/config.yaml under the training section and the CLI. Checkpoints now persist the processor alongside the model so fine-tuned checkpoints load end to end. For realistic training throughput, use a CUDA device (--device cuda).

Fine-Tuned CORD Checkpoint (Drise Cord Fine-tuned Checkpoint/)

The pipeline can run on a locally fine-tuned CORD checkpoint (5-class BIO scheme: O, B-KEY, I-KEY, B-VALUE, I-VALUE) instead of the published jinhybr/OCR-LayoutLMv3-Invoice model. The checkpoint was trained with FUNSD KEY supervision mixed into CORD (--include-funsd, 15 epochs, lr 5e-5, batch 4, grad-accum 2).

Loading is safe by default. Checkpoints saved with transformers 5.x may only ship processor_config.json (no preprocessor_config.json), which older transformers versions reject with AutoProcessor.from_pretrained. Use the project's safe loader instead:

from inference_cord_finetuned import load_model, predict_receipt, set_global_threshold

model, processor, device = load_model("Drise Cord Fine-tuned Checkpoint/")
set_global_threshold(0.7)  # optional; runtime-adjustable confidence threshold

result = predict_receipt(image, words, boxes, model=model, processor=processor, device=device)
print(result["key_value_pairs"])  # KEY -> VALUE pairs + locale-parsed numeric values

The loader falls back to the base microsoft/layoutlmv3-base processor when the local checkpoint lacks preprocessor_config.json, so inference never hard-crashes on the file layout. The production LayoutLMv3InferenceService uses the same logic automatically (set model.checkpoint_path in configs/config.yaml).

Benchmark the checkpoint on the CORD test split (token-level, comparable to the Kaggle 0.868 token-F1 figure):

python scripts/benchmark_cord_finetuned.py --split test --batch-size 8
python scripts/benchmark_cord_finetuned.py --split test --batch-size 8 --tune   # threshold sweep

Results: 0.8576 token-level F1 (P 0.7513 / R 0.9989), mean non-O confidence 0.9986. See the Fine-Tuned Model Benchmark section for the full table.

The 0.625 → 0.8704 Story

Why I fine-tuned

After four phases of pipeline engineering, DRISE had plateaued at 0.625 masked micro-F1. Layout and constraint ablations were flat, which meant the post-processing stack was no longer the bottleneck — the published jinhybr/OCR-LayoutLMv3-Invoice checkpoint was. To break past the ceiling, the system needed an in-domain model trained on the actual target data distribution (CORD receipts + FUNSD forms).

Why Kaggle

The dev machine (MacBook Air M4, no NVIDIA GPU) cannot train a ~500M-parameter LayoutLMv3 — CPU training ran 40 minutes/epoch with validation F1 stuck at 0.0. Kaggle's free Tesla T4 with 16 GB VRAM and 30 weekly hours was the remote training environment.

The core challenge

The fine-tuning harness silently produced val_f1 = 0.0000 for every epoch while train loss collapsed to ~0. Root cause: the naver-clova-ix/cord-v2 parser read compact value dictionaries from gt_parse (which has no words key), so every training sample fell back to all-O labels — the model could only ever predict background. The fix parsed word-level OCR from valid_line (with is_key → B-KEY), plus a nielsr/funsd split fallback (validation → test). Three more gotchas: Kaggle path mismatches, poisoned notebook kernels, and numpy-version pins that broke the pre-installed stack.

Results

Metric	Value
Fine-tuned validation F1 (15 epochs)	0.8704
Token-level F1 on CORD test	0.8576
Precision / Recall (CORD test)	0.7513 / 0.9989
Mean non-O confidence	0.9986
Confidence threshold (sweep-verified)	0.7

The full debugging chronicle — every red herring, the parser bug in detail, the successful training run, checkpoint download/integration issues, and step-by-step reproduction — is documented in KAGGLE_TRAINING_CHRONICLE.md.

Known Limitations

Limitation	Detail
OCR ceiling	Severely degraded scans (heavy noise, sub-100 DPI, mixed orientation) produce low-confidence tokens that downstream models cannot reliably recover
Domain generalization	Defaults to the fine-tuned Drise Cord Fine-tuned Checkpoint/ (CORD receipts + FUNSD forms); performance on out-of-distribution document types will degrade without targeted fine-tuning (swap via model.checkpoint_path in configs/config.yaml)
Ground-truth quality ceiling	FUNSD forms were force-fit into the invoice schema during dataset conversion, producing unreliable total_amount/vendor labels (years, card numbers); CORD conversions emit duplicate empty-description line items. The annotation loader normalizes the CORD artifacts, but the FUNSD labels should be regenerated for a fair score
Multi-page joining	Pages are processed independently — cross-page field references (e.g., total on page 2 referencing items on page 1) are not currently resolved (conflicts are detected and warned)
Table structure	Table cells are extracted, but row/column/span relationships are not reconstructed in the output schema

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Contact

Designed and built by Purvansh Sahu.

If you find this project useful or have suggestions, feel free to open an issue or reach out directly.

• GitHub: @purvanshh
• Email: purvanshhsahu@gmail.com

Footnotes

1. MarketsandMarkets, Document AI Market — Global Forecast to 2030: $14.66B (2025) → $27.62B (2030), 13.5% CAGR. https://www.marketsandmarkets.com/Market-Reports/document-ai-market-195513136.html ↩