AI Sign Language Translator using LLM Summarization Pipeline | Like AWS pipe line of Alexa smart assistant

[Jayesh-Dev21]

AI Sign Language Translator

🌐 Overview:

This project is focused on building an AI-powered web application that can translate sign language gestures into spoken or written text in real-time — and potentially vice versa. The tool aims to bridge communication gaps between the deaf/hard-of-hearing community and non-signers, leveraging computer vision, machine learning, and natural language processing.

🎯 Core Objective:

Create an accessible, intuitive platform that uses a webcam to detect sign language gestures, classify them using AI models, and convert them into spoken or written language in real-time. Optionally, support bidirectional translation (text/speech → sign animation via 3D avatar or video snippets).

📌 Key Features (to be implemented):

Live webcam input for gesture capture

Sign language gesture detection using computer vision

Gesture-to-text/speech translation

Multilingual text output (English, Hindi, etc.)

Optional: Text-to-sign via animations or pre-rendered gesture video

Optional: Gesture dictionary lookup and real-time suggestions

🔍 Target Sign Languages:

Start with ASL (American Sign Language) or ISL (Indian Sign Language)

Modular model design for supporting other sign languages in the future

🛠️ Suggested Tech Stack:

Frontend: React / Vue.js for real-time UI

Backend: Node.js / Python (FastAPI, Flask)

AI/ML:

    Gesture Recognition: MediaPipe Hands, OpenCV, TensorFlow, or PyTorch

    Classification: LSTM/CNN or Transformers on skeletal/landmark data

    Text/Speech: GPT or TTS APIs for output (Google TTS, ElevenLabs, etc.)

Hosting: Vercel / Netlify (frontend), Render / Railway / GCP / AWS Lambda (backend)

Dataset: RWTH-BOSTON, WLASL, or custom ISL datasets

💡 Why Build This?

Accessibility-first innovation — bridges communication between hearing and non-hearing individuals

Encourages inclusivity in tech and education

A playground for combining AI + computer vision + NLP + real-time systems

Can scale to other uses: medical, education, customer service, public spaces

🧠 Challenges & Innovation Opportunities:

High accuracy in real-time gesture classification

Handling gesture variations and fluid transitions

Building or sourcing high-quality sign language datasets

Cross-lingual and cultural considerations in translation