GestureGo - Breaking communication barriers with sign language technology

A real-time American Sign Language (ASL) recognition system using computer vision and deep learning to translate hand gestures into text.

✨ Features

• Real-time Recognition: Live ASL gesture recognition through webcam
• Hand Detection: Accurate hand landmark detection using MediaPipe
• Deep Learning Model: Custom CNN model trained on ASL alphabet dataset
• High Accuracy: Optimized model for reliable gesture classification
• User-friendly Interface: Simple and intuitive real-time display
• Confidence Scoring: Shows prediction confidence for each gesture
• Multi-platform: Works on Windows, macOS, and Linux

🛠 Tech Stack

Machine Learning & AI

• TensorFlow/Keras - Deep learning framework for model training and inference
• MediaPipe - Hand landmark detection and tracking
• NumPy - Numerical computations and array operations

Computer Vision

• OpenCV - Real-time computer vision and image processing
• PIL/Pillow - Image manipulation and preprocessing

Model Architecture

• MobileNetV2 - Efficient CNN architecture for real-time inference
• Transfer Learning - Pre-trained weights fine-tuned for ASL recognition
• Data Augmentation - Enhanced training with image transformations

Development Tools

• Python 3.10 - Primary programming language
• Jupyter Notebooks - Model development and experimentation
• Git - Version control

🚀 Installation

1. Clone the repository

git clone https://github.com/rashiaggarwal06/GestureGo_Sign_Detection_And_Recognition_System.git
cd sign-language

2. Create virtual environment

python -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

3. Install dependencies

pip install -r requirements.txt

🎯 Usage

Real-time Recognition

python realtime.py

• Point your webcam at ASL hand gestures
• The system will detect and classify gestures in real-time
• Press 'q' to quit the application

Training Your Own Model

python train.py

• Ensure your dataset is organized in the correct folder structure
• The script will train a new model and save it as asl_mobilenet_full.keras

📁 Project Structure

sign-language/
├── realtime.py              # Real-time recognition script
├── train.py                 # Model training script
├── asl_mobilenet_full.keras # Pre-trained model
├── classes.txt              # Class labels
├── requirements.txt         # Python dependencies
├── Demo.mov                 # Demo video
├── main.ipynb              # Jupyter notebook for experimentation
└── README.md               # This file

🎬 How It Works

1. Hand Detection: MediaPipe detects hand landmarks in real-time
2. Region Extraction: Extracts hand region with bounding box
3. Preprocessing: Resizes and normalizes the image for model input
4. Prediction: MobileNetV2 model classifies the gesture
5. Display: Shows the predicted letter with confidence score

📊 Model Performance

• Architecture: MobileNetV2-based CNN
• Input Size: 160x160 RGB images
• Classes: 26 ASL alphabet letters
• Accuracy: ~95%+ on test dataset
• Inference Speed: Real-time (30+ FPS)

🔧 Configuration

You can modify the following parameters in realtime.py:

IMG_SIZE = (160, 160)        # Input image size for model
min_detection_confidence=0.6  # Hand detection threshold
min_tracking_confidence=0.5   # Hand tracking threshold
prediction_threshold=0.5      # Minimum confidence for predictions

🤝 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 team for the excellent hand detection framework
• TensorFlow team for the deep learning framework
• ASL alphabet dataset contributors
• Open source community for various tools and libraries

Screenshot

📧 Contact

Rashi Aggarwal

• GitHub: Rashi Aggarwal
• Email: rashiaggarwalofficial@gmail.com

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