Real-Time Sign Language Recognition and Translation Using MediaPipe and Random Forests for Inclusive Communication

Communication barriers faced by deaf and speech-impaired individuals necessitate innovative solutions for effective interaction with the hearing community. This research proposes a novel approach to sign language recognition and translation by leveraging Random Forests and MediaPipe technology. MediaPipe facilitates real-time hand gesture tracking and extraction of hand landmarks crucial for sign language interpretation. Subsequently, a Random Forest classifier is employed to recognize the signs depicted by the tracked gestures accurately. Upon successful recognition, the system seamlessly translates the sign language into written words and text, fostering enhanced communication accessibility for the deaf and speech-impaired community. Integrating advanced machine learning techniques with real-time tracking technology offers promising avenues for breaking down communication barriers and promoting inclusivity.