🤟 Sign Language Interpreter

A real-time sign language recognition system built with Python, MediaPipe, TensorFlow, and Streamlit. This application can detect and interpret sign language gestures in real-time using computer vision and machine learning.

🌟 Features

• Real-time Hand Detection: Uses MediaPipe for accurate hand landmark detection
• Sign Language Recognition: Supports 29 gestures including:
  • Numbers: 1, 2, 4, 5, 6, 7, 8
  • Letters: A-X (excluding E)
• Live Webcam Processing: Real-time gesture recognition through webcam
• Interactive Web Interface: Built with Streamlit for easy use
• Adjustable Confidence: Configurable detection confidence threshold
• Optimized Performance: Fast response with temporal prediction smoothing

🚀 Quick Start

Prerequisites

• Python 3.8+
• Webcam
• Git

Installation

1. Clone the repository

git clone https://github.com/yourusername/ai-sign-language-interpreter.git
cd ai-sign-language-interpreter

2. Install dependencies

pip install -r requirements.txt

3. Run the application

streamlit run simple_sign_interpreter.py

4. Open your browser and navigate to http://localhost:8501

🎯 Usage

1. Start the application using the command above
2. Allow camera access when prompted by your browser
3. Click "START" to begin webcam capture
4. Show hand gestures to the camera
5. Adjust confidence threshold using the sidebar slider for better detection
6. View real-time predictions in the "Current Sign" panel

📁 Project Structure

ai-sign-language-interpreter/
├── simple_sign_interpreter.py    # Main Streamlit application
├── sign_utils.py                 # Core prediction and landmark detection
├── models/
│   ├── sign_model.tflite        # Trained TensorFlow Lite model
│   └── labels.txt               # Class labels for gestures
├── requirements.txt             # Python dependencies
├── .gitignore                  # Git ignore file
└── README.md                   # Project documentation

🔧 Technical Details

Architecture

• Frontend: Streamlit web interface
• Computer Vision: MediaPipe for hand landmark detection
• Machine Learning: TensorFlow Lite for gesture classification
• Real-time Processing: Optimized prediction pipeline with temporal smoothing

Model Information

• Input: 63-dimensional hand landmark features (21 landmarks × 3 coordinates)
• Output: 29 sign language gesture classes
• Format: TensorFlow Lite (.tflite) for optimized inference
• Performance: Real-time processing at 30+ FPS

Key Components

1. MediaPipeLandmarksModel: Handles hand detection and landmark extraction
2. SignLanguagePredictor: Manages model inference and prediction smoothing
3. Streamlit Interface: Provides user-friendly web interface

🎛️ Configuration

Adjustable Parameters

• Detection Confidence: Minimum confidence for hand detection (default: 0.5)
• Tracking Confidence: Minimum confidence for hand tracking (default: 0.5)
• Prediction Threshold: Minimum confidence for gesture prediction (default: 0.5)
• Buffer Size: Number of frames for prediction smoothing (default: 8)

🤝 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

• MediaPipe for hand landmark detection
• TensorFlow for machine learning framework
• Streamlit for the web interface
• OpenCV for computer vision utilities

📞 Support

If you encounter any issues or have questions:

1. Check the Issues page
2. Create a new issue with detailed description
3. Include system information and error messages

🔮 Future Enhancements

• Support for more sign language gestures
• Multi-hand gesture recognition
• Sign language sentence formation
• Mobile app version
• Real-time translation to multiple languages