DEAF AND DUMP SIGN RECOGNITION

Abstract

This study intends to create a comprehensive system for deaf and mute sign language recognition that supports many languages such as Telugu, Tamil, and English. Taking advantage of advances in computer vision and machine learning, the proposed system uses deep learning techniques to accurately read and translate sign language motions to text or speech. The method tries to accomplish strong multilingual recognition by training models on varied datasets containing sign language samples from various linguistic backgrounds. The system's real-time video processing capabilities allow it to provide quick feedback and facilitate communication between those with hearing and speech difficulties and those who use spoken languages. Through this multimodal approach, the proposed system aims to bridge communication gaps, improve accessibility, and promote inclusivity for people with hearing and speech difficulties across linguistic boundaries.