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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM64749
Volume:
10 |
6 |
Jun 2026
Pages:
1-3
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Department of Computer Science
Galgotias University
As technology advances each year, humanity continues to develop innovative tools aimed at improving lives, especially for individuals with disabilities. Our project focuses on easing communication for people with speech impairments by proposing a sign language interpreter that automatically converts sign language into text output. For those unable to speak, sign language serves as a primary mode of expressing ideas and emotions. However, this form of communication is challenging for many people to understand, making interaction difficult. Recognizing sign language accurately is complex due to the variety of hand movements and gestures involved, and achieving high accuracy at a low cost is a significant challenge. Our solution proposes a system that leverages deep learning techniques, particularly Convolutional Neural Networks (CNNs), to recognize sign language gestures accurately. By using pre-trained hand-tracking models like MediaPipe, the system ensures robust detection in various environments
KEYWORDS
The system was successfully developed, implemented, tested, and deployed. This paper highlighted the importance of sign language recognition systems for individuals who are deaf or hearing-impaired. It demonstrated the capability to accurately recognize sign language gestures in real-time. However, enhancing the model’s effectiveness and reliability may require additional data collected under various environmental conditions. Currently, the system is limited to recognizing static ASL alphabet gestures and does not account for the dynamic and continuous aspects of sign language, which involve complex movements, facial expressions, and body language. Future research should focus on extending the system's capabilities to recognize dynamic gestures for a more comprehensive mode of communication. The system has been specifically trained and evaluated on the ASL alphabet, but since sign languages differ across regions and countries, each possessing distinct vocabulary and grammar, the model's performance with other sign languages remains untested. Further exploration and adaptation are necessary to ensure its applicability across different sign languages.
[1] Nair, A.V. and Bindu, V., “A review on Indian sign language recognition”, International Journal of Computer Applications, 73(22), 2013. [2] Sailee Brahme, “READING THE SIGNS:SIGNS LANGUAGE”, April 21, 2017, available online at http://forreadingaddicts.co.uk/culture/reading-signs-signlanguage/ 18395 [3] Republic World, Press Trust of India, "India’s First Sign Language Dictionary Released”, Mumbai, March 23, 2018 [4] Lucy Sweeney, “Gloves that convert sign language to speech ‘to empower the deaf community’ ”, ABC News, April 2016. [5] Divyangjan, “Indian Sign Language Research and Training Centre (ISLRTC), Department of Empowerment of Persons with Disabilities, Ministry of Social Justice & Empowerment, Government of India, online available at http://205.147.97.190/islrtcapp/ [6] Sanjay Kumar, “First Indian Sign Language Dictionary of 3000 Words”, Press Information Bureau, Government of India, Ministry of Social Justice & Empowerment, 23-March-2018 [7] Strong, M. and Prinz, P.M., “A Study of the Relationship Between American Sign Language and English Literacy”, The Journal of Deaf Studies and Deaf Education, 2(1), pp.37-46, 1997 [8] Divya Deora, D. and Bajaj, N., “Indian sign language recognition”, In Emerging Technology Trends in Electronics, Communication and Networking (ET2ECN), 2012 1st International Conference on (pp. 1-5). IEEE, December 2012.
Publication Date
6/10/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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No funding information available for this research.
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