“ SIGNBRIDGE REALTIME SIGN LANGUAGE INTERPRETATION FOR INCLUSIVE COMMUNICATION”
Keywords:
Sign Language, Real-time Interpretation, Machine Learning, Computer Vision, Accessibility, Assistive Technology, Gesture Recognition, Inclusive Communication, Deep Learning, Natural Language Processing. .Abstract
This paper presents Signbridge, a novel system designed for realtime interpretation of sign language into spoken and written language, fostering inclusive communication for the deaf and hard-of-hearing community. Signbridge leverages advanced computer vision techniques, including deep learning-based pose estimation and gesture recognition, to accurately capture and translate sign language gestures. Furthermore, it incorporates natural language processing (NLP) for contextual understanding and fluency enhancement. The system's realtime capabilities are crucial for seamless communication in diverse settings, bridging the gap between sign language users and those unfamiliar with it. We evaluate the system's performance on a custom-collected dataset and demonstrate its potential for improving accessibility and inclusivity.
