SignBridge

SignBridge is an advanced, full-stack web application that bridges communication between hearing and non-hearing individuals. It provides real-time sign language recognition (Sign-to-Text, STT) and text-to-sign translation (TTS) using modern machine learning (YOLOv8), a Python FastAPI backend, and a Next.js/React frontend. The project includes a curated set of SVG assets for American Sign Language (ASL) fingerspelling.

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Table of Contents

• Features
• Architecture
• Directory Structure
• Setup & Installation
• Usage
• Backend API
• Frontend App
• SVG Assets
• Utility Scripts
• Contributing
• License

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Features

• Real-time Sign-to-Text (STT): Uses webcam and YOLOv8 to recognize sign language gestures and convert them to text.
• Text-to-Sign (TTS): Converts typed text into animated ASL fingerspelling using SVG images.
• Modern UI: Responsive, accessible, and visually appealing interface built with Next.js, React, and Tailwind CSS.
• High Accuracy: ML backend achieves up to 99% accuracy on benchmark datasets.
• Extensible: Modular codebase for easy addition of new features or sign languages.

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Architecture

[User]
   │
   ▼
[Frontend (Next.js/React)]
   │        ▲
   ▼        │
[Backend FastAPI API (YOLOv8)]
   │
   ▼
[ML Model, SVG Assets]

• Frontend: Next.js app for UI, TTS, and STT flows. Communicates with backend for STT and serves SVGs for TTS.
• Backend: FastAPI API for sign recognition (STT) using YOLOv8. Serves predictions to frontend.
• Assets: SVG images for each ASL letter (A-Z) for TTS.

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Directory Structure

sign_bridge/
├── backend/
│   ├── STT/           # FastAPI server for sign-to-text
│   │   ├── main.py    # Main FastAPI app (YOLOv8 inference)
│   │   ├── uploads/   # Uploaded images (temp)
│   │   └── weights/   # ML model weights (best.pt)
│   └── TTS/
│       └── img/       # SVGs for ASL letters (A-Z)
├── frontend/
│   ├── app/           # Next.js app source
│   │   ├── components/  # React UI components
│   │   ├── pages/       # Page routes (TTS, STT, Landing)
│   │   ├── api/         # API routes (SVG serving)
│   │   └── utils/       # Utility functions
│   ├── public/        # Static assets (icons, images)
│   └── ...            # Config, package.json, etc.
├── convert_svgs_to_white.py # Utility script for SVG color conversion
└── README.md

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Setup & Installation

Prerequisites

• Backend: Python 3.8+, pip
• Frontend: Node.js 18+, npm/yarn/pnpm

1. Backend (STT)

# Install dependencies
pip install -r backend/STT/requirements.txt

# Run the backend server
cd backend/STT
uvicorn main:app --reload

• The server runs on http://localhost:8000 by default.

2. Frontend (TTS/STT UI)

# Install dependencies
cd frontend
npm install

# Run the frontend development server
npm run dev

• The app runs on http://localhost:3000 by default.

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Usage

Text-to-Sign (TTS)

• Navigate to /pages/tts in the app.
• Enter text (A-Z) to see animated ASL fingerspelling.
• Copy or download the generated sign sequence.

Sign-to-Text (STT)

• Navigate to /pages/stt in the app.
• Start your webcam and begin signing.
• The backend will process frames and return recognized signs as text.

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Backend API

/predict (POST)

• Description: Accepts an image (form-data, key: image), returns detected sign(s).
• Response:

{
  "predictions": [
    { "class": "A", "confidence": 0.98, "bbox": [x1, y1, x2, y2] },
    ...
  ]
}

• Model: YOLOv8, expects weights at backend/STT/weights/best.pt.

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Frontend App

• Framework: Next.js 15, React 19, Tailwind CSS
• Key Pages:
  • / (Landing): Project intro, features, navigation
  • /pages/tts: Text-to-Sign UI
  • /pages/stt: Sign-to-Text UI
• Components: Modular, reusable (see app/components/)
• API Route: /api/signs/[letter] serves SVG for each ASL letter (A-Z)

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SVG Assets

• Located in backend/TTS/img/
• Each file (A.svg, ..., Z.svg) represents an ASL fingerspelling letter
• Used for TTS animation in the frontend

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Utility Scripts

convert_svgs_to_white.py

• Batch converts all black fills/strokes in SVGs to white for dark mode compatibility.
• Usage:

  python convert_svgs_to_white.py

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Contributing

1. Fork the repo and create a feature branch.
2. Make your changes (with clear commits).
3. Test both backend and frontend.
4. Submit a pull request with a clear description.

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License

This project is open-source and available under the MIT License.