Bidirectional Indian Sign Language Converter

This project introduces an AI-driven web platform designed for the real-time recognition and translation of Indian Sign Language (ISL), aiming to bridge the communication divide between the deaf-mute community and the wider public. The system supports bidirectional communication by translating ISL gestures into regional language text and speech, and conversely, converting spoken or written input into pre-recorded sign language representations. By leveraging YOLO-based deep learning models alongside advanced computer vision techniques, the platform accurately detects and interprets Indian Sign Language (ISL) gestures in real time. Achieving an accuracy of approximately 90 %, the system demonstrates significant promise in addressing communication barriers experienced by individuals with hearing and speech impairments. By integrating AI into an accessible web-based interface, this project highlights the transformative role of technology in fostering inclusivity and enhancing social interactions.

Authors

Publication Details

Published
2025-08-07
DOI
https://doi.org/10.1109/netact65906.2025.11188187
Citations
1
Primary Topic
Hand Gesture Recognition Systems
Type
article
Field-Weighted Citation Impact
1.94
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article

Bidirectional Indian Sign Language Converter

K. C. Jithin, Nagalla Nikhitha, Sandeep Kumar S, A. K. et al.
1 citations
Hand Gesture Recognition Systems
1.94
article

Bidirectional Indian Sign Language Converter

K. C. Jithin, Nagalla Nikhitha, Sandeep Kumar S, A. K., Rahul Dhanaraj, Shins John Jaison
article en
1 citations

Abstract

This project introduces an AI-driven web platform designed for the real-time recognition and translation of Indian Sign Language (ISL), aiming to bridge the communication divide between the deaf-mute community and the wider public. The system supports bidirectional communication by translating ISL gestures into regional language text and speech, and conversely, converting spoken or written input into pre-recorded sign language representations. By leveraging YOLO-based deep learning models alongside advanced computer vision techniques, the platform accurately detects and interprets Indian Sign Language (ISL) gestures in real time. Achieving an accuracy of approximately 90 %, the system demonstrates significant promise in addressing communication barriers experienced by individuals with hearing and speech impairments. By integrating AI into an accessible web-based interface, this project highlights the transformative role of technology in fostering inclusivity and enhancing social interactions.

Openalex Percentile: Top 12%
Hand Gesture Recognition Systems
1.94
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