Published Paper Details:

This project aims to develop a Sign Language Converter, a technological solution designed to bridge the communication gap between Deaf and hearing individuals. The system uses advanced gesture recognition and machine learning algorithms to interpret hand movements, facial expressions, and body language associated with sign languages, such as American Sign Language and British Sign Language. By capturing these gestures through cameras, sensors, or glove-based systems, the converter translates them into spoken or written text in real-time. Additionally, the project explores the reverse translation process, converting text to sign language using animated avatars. Key challenges addressed include the variability in regional sign languages and the structural differences between sign and spoken languages. The system leverages AI and deep learning to increase gesture recognition accuracy. This Sign Language Converter has the potential to significantly improve accessibility for the Deaf community significantly, enabling seamless communication across social, educational, and professional environments.