The Hand Language Translator is an innovative assistive technology project that bridges the communication gap between sign language users and non-signers. Using computer vision and machine learning, the system recognizes hand gestures in real-time and translates them into both written text and spoken audio.
This project demonstrates the practical application of artificial intelligence in solving real-world accessibility challenges. By combining OpenCV for image processing, decision tree-based machine learning models for gesture recognition, and Google Text-to-Speech for audio output, the system creates a complete translation pipeline that operates in real-time.
The translator is designed to be accessible and user-friendly, requiring only a standard webcam for operation. It showcases proficiency in computer vision, machine learning model selection and optimization, and the integration of multiple technologies.