Description
MediaPipe enables real-time hand tracking and gesture recognition, making it useful for sign language translation, virtual interaction, and gesture-based control.
It uses machine learning to detect 21 key points on the hand in 3D space, running efficiently on mobile and web platforms. Lightweight and highly adaptable, MediaPipe integrates with TensorFlow, OpenCV, and other frameworks, making it a powerful tool for AI-driven hand recognition and interactive applications.
This can be seamlessly integrated into some features of my sign language learning app. Just as MediaPipe can automatically decompose human body movements, sign language grammar is also structured around five key components: Handshape, Hand Movement, Hand Orientation, Body Posture, Facial Expressions:
By leveraging MediaPipe, we can analyze and assess sign language movements with greater accuracy while enabling engaging interactive features that enhance the learning experience.