Sign Language Translation System using Natural Language Processing (NLP)

Sign language functioned as a primary mode of communication for the hearing-impaired community, yet language barriers often constrained their interaction with the wider population. This study proposed a Sign Language Translation System that utilized Natural Language Processing (NLP) techniques to interpret and convert hand gestures into corresponding textual or spoken language. The system recorded dynamic and static gestures using camera-based input and processed the data through preprocessing, feature extraction, and classification steps. NLP algorithms examined the sequential patterns of gestures to generate meaningful and contextually accurate language outputs. Experimental evaluation showed that the system achieved an overall translation accuracy of 92% for single-word gestures and 87% for sentence-level gestures across multiple languages. These results confirmed the system’s effectiveness in converting gestures into coherent text and speech, underscoring the potential of integrating gesture recognition with NLP to enhance accessibility, improve communication efficiency, and bridge the gap between sign language users and non-users.