This project aims to develop a sign language detection system using computer vision and machine learning techniques. The system is designed to interpret hand gestures captured through a webcam in real-time and classify them into corresponding sign language gestures.
- Real-time hand detection and tracking using webcam input.
- Classification of detected hand gestures into predefined sign language categories.
- User-friendly interface for collecting training data through webcam feed.
- Support for customization and extension with additional sign language categories.
- Clone the repository to your local machine:
git clone [email protected]:Priyanshuparth/HandSpeak-Visual-Gesture-Interpreter.git
- Install the required dependencies:
pip install -r requirements.txt
- Download the pre-trained model and label files (if available) and place them in the appropriate directories.
Run the main Python script to start the sign language detection system:
python main.pyFollow the on-screen instructions to interact with the system:
- Use webcam input to capture hand gestures in real-time.
- Press specific keys to exit the screen press
q - To collect the images press
s
To collect training data for the sign language detection model, follow these steps:
- Run the data collection script:
python data_collection.py
- Follow the instructions to capture and save hand gesture images using the webcam.
Contributions to this project are welcome! If you have ideas for improvements or new features, please submit a pull request or open an issue on GitHub.
This project is licensed under the MIT License.
- The hand detection and tracking functionalities in this project are powered by the cvzone library.
