Turkish Sign Language Recognition

Real-time Turkish Sign Language recognition web application with React.js frontend and FastAPI backend, using deep learning models for TID (Turkish Sign Language) gloss recognition.

🌟 Features

• Real-time Recognition: Live webcam video processing and sign language detection
• Turkish Interface: Full Turkish language support in the UI
• RESTful API: Clean FastAPI backend with comprehensive endpoints
• Modern UI: Responsive React.js frontend with TailwindCSS
• Video Processing: Real-time frame capture and analysis
• Prediction History: Track and display recognition results over time

🏗️ Project Structure

├── backend/           # FastAPI server with PyTorch model
│   ├── main.py       # Main API server
│   ├── requirements.txt
│   └── README.md
├── frontend/          # React.js web application
│   ├── src/
│   │   ├── components/
│   │   ├── App.jsx
│   │   └── main.jsx
│   ├── package.json
│   └── README.md
└── README.md         # This file

🚀 Quick Start

Prerequisites

• Python 3.8+ (for backend)
• Node.js 16+ and npm (for frontend)
• Webcam-enabled device

Backend Setup

cd backend
pip install -r requirements.txt
python main.py

The API server will start at http://localhost:8000

Frontend Setup

cd frontend
npm install
npm run dev

The web application will be available at http://localhost:3000

🎯 Usage

1. Start Backend: Run the FastAPI server first
2. Start Frontend: Launch the React development server
3. Allow Camera Access: Grant webcam permissions when prompted
4. Begin Recognition: Click "Kamerayı Başlat" to start the camera
5. Perform Signs: Make Turkish sign language gestures in front of the camera
6. View Results: See real-time predictions and history in the interface

🤖 Supported Turkish Sign Language Glosses

The system recognizes common TID glosses including:

• Greetings: merhaba, günaydın, iyi akşamlar, görüşürüz
• Courtesy: teşekkür ederim, lütfen, özür dilerim
• Basic Responses: evet, hayır, iyiyim, nasılsın
• Family: anne, baba, kardeş, arkadaş
• Professions: öğretmen, doktor, polis
• Objects: su, yemek, ev, okul, kitap, telefon, araba

🛠️ Technology Stack

Backend

• FastAPI: Modern, fast web framework for APIs
• PyTorch: Deep learning framework for model inference
• OpenCV: Computer vision library for image processing
• Pillow: Python imaging library
• Uvicorn: ASGI server for FastAPI

Frontend

• React.js 18: Modern JavaScript library for UI
• Vite: Fast build tool and development server
• TailwindCSS: Utility-first CSS framework
• Axios: Promise-based HTTP client
• HTML5 Canvas: Video frame processing

🔧 API Endpoints

Method	Endpoint	Description
GET	/	API information and status
GET	/health	Health check endpoint
POST	/predict	Upload image file for prediction
POST	/predict-base64	Base64 image prediction
GET	/glosses	Get available TID glosses

📱 Browser Requirements

• Modern browser with HTML5 support
• Webcam access permissions
• JavaScript enabled
• Canvas API support

🤝 Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• Turkish Sign Language (TID) research community
• AUTSL dataset contributors
• Open source libraries and frameworks used in this project