Signbridge Realtime Sign Language Interpretation for Inclusive Communication
Year: 2025
Type: article
Abstract: ABSTRACT :-This paper presents Signbridge, a novel system designed for realtime interpretation of sign languageinto spoken and written language, fostering inclusive communication for the deaf and hard-of-hearingcommunity. Signbridge leverages advanced computer vision techniques, including deep learningbasedpose estimation and gesture recognition, to accurately capture and translate sign languagegestures. Furthermore, it incorporates natural language processing (NLP) for contextual understandingand fluency enhancement. The system's realtime capabilities are crucial for seamless communicationin diverse settings, bridging the gap between sign language users and those unfamiliar with it. Weevaluate the system's performance on a custom-collected dataset and demonstrate its potential forimproving accessibility and inclusivity. Keywords: Sign Language, Real-time Interpretation, Machine Learning, Computer Vision,Accessibility, Assistive Technology, Gesture Recognition, Inclusive Communication, Deep Learning,Natural Language Processing.
Authors: Divyesh Khairnar, Shravan Londhe, Sahil Gaikwad, Tanmay Shewale, Prof. Vidya Kale, Prof.Mr.Mahesh P. Bhandakkar
Language: English
Subfield: Human-Computer Interaction
Field: Computer Science
Domain: Physical Sciences
SDG: Reduced inequalities
Open Access status: green
OpenAlex ID: W6911920952
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