A Communication Bridge Using Sign Language Detection for Hearing Impaired: A Study on Southern Region of India

DOI

10.2991/978-94-6239-616-6_35How to use a DOI?

Keywords

Tamil Sign Language; Sign Language Recognition; Deep Learning; Computer Vision; Natural Language Processing; Text to Speech; Gesture Recognition; Assistive Technology

Abstract

Sign language is one of the most important means of communication for people with hearing and speech impairments. In recent years, research on Sign Language Recognition has advanced rapidly, especially for widely studied languages like American Sign Language, Indian Sign Language, and Arabic Sign Language. The availability of benchmark datasets and the rise of powerful computer vision and deep learning techniques have made it possible to design systems that are accurate, fast, and increasingly practical for real-world use. In contrast, Tamil Sign Language (TSL), which plays a vital role for the Tamil-speaking community, has not received the same attention. The lack of standardized datasets, linguistic studies, and recognition frameworks has slowed down progress in this area. As a result, technologies that work well for ASL or ISL cannot be directly applied to TSL without significant adaptation. This survey takes a closer look at how sign language recognition has evolved over the years. It reviews both sensor-based approaches, which were among the first attempts at capturing gestures, and vision-based approaches. The discussion covers the transition from early handcrafted techniques to modern architectures such as Convolutional Neural Networks, Recurrent Neural Networks, and more recent Transformer-based models. The survey also highlights how multimodal systems, which integrate Natural Language Processing and Text-to-Speech, are helping to make recognition technologies more inclusive by supporting both literate and non-literate users.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - S. Balaji
AU  - J. Sakthivel
AU  - J. Joseph Augustine
AU  - K. Thanigaivelan
PY  - 2026
DA  - 2026/03/31
TI  - A Communication Bridge Using Sign Language Detection for Hearing Impaired: A Study on Southern Region of India
BT  - Proceedings of the International Conference on Artificial Intelligence and Secure Data Analytics (ICAISDA 2025)
PB  - Atlantis Press
SP  - 462
EP  - 473
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6239-616-6_35
DO  - 10.2991/978-94-6239-616-6_35
ID  - Balaji2026
ER  -