Real-Time Sign Language Recognition System (EchoFlow)

Overview

Welcome to the Real-Time Sign Language Recognition System project, developed by Ezequiel Bellver. This project aims to develop a real-time sign language recognition (SLR) system specifically tailored for British Sign Language (BSL) gestures, focusing on a studio setup with optimal lighting conditions. The system will utilize computer vision and deep learning techniques to accurately interpret BSL gestures from live video input.

Project Structure

The project follows a standard directory structure:

• project_root/
  • data/
    • bsl_corpus/
  • models/
  • src/
    • preprocessing.py
    • feature_extraction.py
    • model.py
    • inference.py
    • utils.py
  • notebooks/
  • tests/
  • requirements.txt
  • README.md
  • LICENSE

Getting Started

To get started with the project, follow these steps:

1. Clone the repository: git clone <repository_url>
2. Install the required dependencies: pip install -r requirements.txt
3. Explore the source code in the src/ directory to understand the project structure and functionality.
4. Experiment with Jupyter notebooks in the notebooks/ directory for data exploration and algorithm testing.
5. Run unit tests in the tests/ directory to ensure the correctness of the code.

Contributing

Contributions to the project are welcome! If you find any issues or have suggestions for improvements, please open an issue or submit a pull request. Make sure to follow the project's coding conventions and guidelines.

License

This project is licensed under the MIT License.