Artificial Intelligence / Machine Learning
This project presents a Sign Language Recognition and Translation System designed to bridge the communication gap between deaf and hearing individuals. The system enables two-way communication by converting sign language into text and speech, and vice versa. It uses machine learning and computer vision techniques, including YOLOv8 for hand sign detection and MediaPipe for real-time hand tracking and gesture recognition. The backend is developed in Python with a Flask-based web application, while the frontend uses HTML, CSS, and JavaScript. The system also integrates text-to-speech functionality and supports real-time interaction through a user-friendly interface, making it suitable for practical communication and learning purposes.
SignBridge: AI-Based Sign Language Recognition and Translation System
This project presents a Sign Language Recognition and Translation System designed to bridge the communication gap between deaf and hearing individuals. The system enables two-way communication by converting sign language into text and speech, and vice versa. It uses machine learning and computer vision techniques, including YOLOv8 for hand sign detection and MediaPipe for real-time hand tracking and gesture recognition. The backend is developed in Python with a Flask-based web application, while the frontend uses HTML, CSS, and JavaScript. The system also integrates text-to-speech functionality and supports real-time interaction through a user-friendly interface, making it suitable for practical communication and learning purposes.