Hand Sign Detection is a computer vision project that recognizes and classifies different hand gestures or signs using machine learning models. This project can be applied in areas like sign language interpretation, gesture-based controls, and gaming interfaces.
- Real-time Hand Sign Recognition: Detect and classify hand signs from video or live camera feeds.
- Pre-trained Models: Utilizes machine learning models for recognizing common hand gestures.
- Customizable Sign Sets: Easily extend the model to recognize additional hand signs.
- Cross-Platform: Supports TensorFlow, OpenCV, and other deep learning libraries.
- Python
- OpenCV – For real-time video capture and image processing.
- TensorFlow/Keras – For building and training deep learning models.
- MediaPipe – For hand landmark detection and tracking.
- NumPy – For numerical operations.
- Matplotlib/Seaborn – For visualizations and model performance analysis.
- Clone the repository:
git clone https://github.com/stealthwhiz24/hand-sign-detection.git cd hand-sign-detection