Sign-Language-Translator

This project provides a real-time hand gesture recognition system using MediaPipe and deep learning classifiers. It includes a Python application for recognizing hand gestures from webcam input, as well as tools for training and evaluating keypoint and point history classifiers.

Features

• Real-time hand gesture recognition using webcam
• Keypoint and point history classification
• Pre-trained models included
• Modular code structure for easy extension

Project Structure

main.py
hand-gesture-recognition-mediapipe/
    app.py
    keypoint_classification_EN.ipynb
    keypoint_classification.ipynb
    point_history_classification.ipynb
    model/
        keypoint_classifier/
        point_history_classifier/
    utils/

• main.py: Entry point for the project.
• hand-gesture-recognition-mediapipe/app.py: Main application for gesture recognition.
• model/: Contains pre-trained models and related scripts.
• utils/: Utility functions (e.g., FPS calculation).
• Jupyter Notebooks: For training and experimenting with classifiers.

Requirements

• Python 3.10+
• MediaPipe
• OpenCV
• NumPy
• TensorFlow

Install dependencies with:

pip install -r requirements.txt

If requirements.txt is missing, install manually:

pip install mediapipe opencv-python numpy tensorflow

Usage

To run the hand gesture recognition app:

python hand-gesture-recognition-mediapipe/app.py

Training

Use the provided Jupyter notebooks to train or fine-tune classifiers:

• keypoint_classification_EN.ipynb
• keypoint_classification.ipynb
• point_history_classification.ipynb

Model Files

Pre-trained models are stored in:

• model/keypoint_classifier/
• model/point_history_classifier/

License

See hand-gesture-recognition-mediapipe/LICENSE.

Acknowledgements

• MediaPipe
• Original gesture recognition codebase inspiration

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Feel free to contribute or open issues!