Sign Language Recognition Using ML

Snehal Patil; A. Y. Prabhakar; Deepak Ray; Navin Kumar

doi:10.2991/978-94-6239-723-1_6

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Sign Language Recognition Using ML

Authors

Snehal Patil¹, A. Y. Prabhakar²^{, *}, Deepak Ray²^{, *}, Navin Kumar²^{, *}

¹Department of Computer Science and Engineering RIT, Shivaji University, Kolhapur, India

²Department of Electronics and Telecommunication Engineering, Bharati Vidyapeeth Deemed to be University, Pune, India

^*Corresponding author. Email: ayprabhakar@bvucoep.edu.in

^*Corresponding author.

Corresponding Authors

A. Y. Prabhakar, Deepak Ray, Navin Kumar

Available Online 14 July 2026.

DOI: 10.2991/978-94-6239-723-1_6 How to use a DOI?
Keywords: Gradient Descent Algorithm; Adam Optimizer; American Sign Language ASL; Convolutional Neural Network CNN; and Sign Language Recognition SLR[2]
Abstract: Sign language is a vital component of daily life for deaf and mute people who utilize it as a form of communication. Even in those nations where it is accepted as one of the official languages, there are few solutions available in today’s world to make the interactions fluid and straightforward. The most common way to communicate with deaf people is to ask for another person’s help. Because a reliable sign language translator may not always be available, this can be a dangerous job. So, we want to improve both their lives and the lives of others around them. A sign language interpreter might be helpful if there are no other interpreters available. It can recognize and interpret using computer vision and machine learning, and it will also pick up new information on its own, making the process of teaching and upgrading the machine much easier. It can be used in TV shows and news stories for easier rendering. The process would also be easier and more streamlined as there wouldn’t be a need for a mediator.^[2]
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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Volume Title: Proceedings of the International Conference on Responsible, Risk-aware, and Regulated AI (RRRAI 2026)
Series: Advances in Intelligent Systems Research
Publication Date: 14 July 2026
ISBN: 978-94-6239-723-1
ISSN: 1951-6851
DOI: 10.2991/978-94-6239-723-1_6 How to use a DOI?
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.

Cite this article

ris enw bib

TY  - CONF
AU  - Snehal Patil
AU  - A. Y. Prabhakar
AU  - Deepak Ray
AU  - Navin Kumar
PY  - 2026
DA  - 2026/07/14
TI  - Sign Language Recognition Using ML
BT  - Proceedings of the International Conference on Responsible, Risk-aware, and Regulated AI (RRRAI 2026)
PB  - Atlantis Press
SP  - 60
EP  - 68
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6239-723-1_6
DO  - 10.2991/978-94-6239-723-1_6
ID  - Patil2026
ER  -

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