Real Time Sign Language Translation by Leveraging Generative AI

Real-time sign language translation systems use computer vision (CV), natural language processing (NLP), and generative AI to bridge communication gaps between sign language users and non-users. By leveraging pose estimation and gesture recognition, these systems accurately interpret hand, face, and body movements. An AI model then processes this data, translating individual signs while preserving context and semantics. The refined output is converted into natural-sounding speech with tone and emotion for better communication. This AI-driven approach enhances accessibility for the mute and speech-impaired, enabling real-time translation in conversations, media, and education. Unlike traditional methods, it integrates context-aware generative AI for more fluid and meaningful interactions. This study assesses the system's effectiveness in improving communication for speech-impaired individuals, aiming to make interactions more natural and inclusive.