This project integrates sign language recognition to enhance accessibility for individuals with hearing or speech impairments. By using hand gesture recognition and machine learning models, the system interprets signs in real time and converts them into readable or audible outputs, effectively bridging the communication gap.
- Real-time hand gesture recognition
- Conversion of signs to text and speech
- User-friendly interface
- Enhances accessibility and communication for the deaf and mute community
- Easily extendable with custom gestures and models
- Python
- OpenCV
- TensorFlow / Keras
- MediaPipe (for hand tracking)
- Text-to-Speech (TTS) libraries
- Streamlit / Tkinter (optional GUI)
- Capture hand gestures via webcam
- Process frames using computer vision techniques
- Predict sign language using a trained ML model
- Display or vocalize the interpreted output
Contributions are welcome! Feel free to fork the repo and submit a pull request.
📺 Demonstration
Watch the full demonstration on YouTube:
Big shoutout to him!!! DEEPANSHU TOLANI
If you have any questions or would like to contribute to the project, feel free to reach out:
Mr. Deepanshu Tolani
📧 tolanideepanshu@gmail.com
Made with ❤️ to support accessibility and inclusion.