Bridging Communication Gaps: Real-Time Sign Language Translation Using Deep Learning and Computer Vision

K. E. Eswari, Rahuls Raja R. V., Vaibhavan M., Yuva Surya K. S.

2025

Abstract

Owing to a knowledge gap on sign language, deaf and hearing communities face communication issues. The rest of the paper aims to give an overview of the topic and present a viable live solution through a Sign Language Translator based on computer-vision and deep learning. The model utilizes a camera to detect hand gestures, followed by their analysis (CNN) and ultimately producing voice or text. Recognition performance is improved by a large dataset and sentence generation is augmented by NLP methods. This technology is built for changing environments and compensates for occlusion, speed or lighting differences. The excellent accuracy in both experiments indicates that it can be an exciting tool for inclusion.

Paper Citation

in Harvard Style

Eswari K., V. R., M. V. and S. Y. (2025). Bridging Communication Gaps: Real-Time Sign Language Translation Using Deep Learning and Computer Vision. In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25; ISBN 978-989-758-777-1, SciTePress, pages 443-447. DOI: 10.5220/0013899700004919

in Bibtex Style

@conference{icrdicct`2525,
author={K. Eswari and Rahuls V. and Vaibhavan M. and Yuva S.},
title={Bridging Communication Gaps: Real-Time Sign Language Translation Using Deep Learning and Computer Vision},
booktitle={Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25},
year={2025},
pages={443-447},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013899700004919},
isbn={978-989-758-777-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25
TI - Bridging Communication Gaps: Real-Time Sign Language Translation Using Deep Learning and Computer Vision
SN - 978-989-758-777-1
AU - Eswari K.
AU - V. R.
AU - M. V.
AU - S. Y.
PY - 2025
SP - 443
EP - 447
DO - 10.5220/0013899700004919
PB - SciTePress