Hand Sign Recognition for Sign Language Translation

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The Hand Sign Recognition project leverages the power of Python and MediaPipe to interpret human hand gestures in real time and convert them into the corresponding sign language alphabet. This solution aims to bridge the communication gap between sign language users and non-signers, offering a reliable, accurate, and responsive system.

By combining advanced hand-tracking algorithms with gesture classification, this project provides an accessible and inclusive communication tool suitable for educational purposes, accessibility-focused applications, and real-time interaction scenarios.

1. Hand Detection & Tracking - Utilizes MediaPipe’s state-of-the-art hand landmark detection to accurately track finger positions and hand movements in real time.

2. Sign Language Alphabet Conversion - Recognizes various hand shapes and movements, then maps them to the corresponding sign language alphabet, enabling seamless communication.

3. Real-time Processing - Offers low-latency gesture recognition, ensuring instant feedback during conversations or demonstrations.

The Hand Sign Recognition for Sign Language Translation project demonstrates how computer vision can enhance inclusivity in communication. With real-time hand gesture tracking and sign alphabet conversion, it serves as a practical foundation for accessibility tools, education platforms, and assistive communication technologies.