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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM63879
Volume:
10 |
5 |
May 2026
Pages:
1-4
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.
Master of Computer Application
Bapuji Institute of Engineering and Technology, Davanagere
Master of Computer Application
Bapuji Institute of Engineering and Technology, Davanagere
The current project, "Gesture Based Tool For Disabled Using AI" Gesture-based totally verbal exchange structures play a essential function in helping speech- and hearing-impaired people by way of permitting non-verbal interaction through visual signals. This undertaking proposes an AI-based totally gesture verbal exchange tool the use of MediaPipe and system getting to know techniques to recognize and interpret hand gestures in actual time. The device captures live video input via a digicam and makes use of MediaPipe’s hand landmark detection version to extract particular spatial coordinates of hand joints. these landmarks are used as feature inputs to a trained type model that correctly identifies predefined gestures. once a gesture is identified, the machine converts it into corresponding textual content or audio output, enabling seamless conversation with non-disabled users. The proposed approach gets rid of the need for wearable sensors or outside hardware, making the system cost-effective and person-pleasant. The model is designed to function successfully under actual-time constraints whilst keeping high accuracy. This AI-pushed solution may be deployed in instructional institutions, healthcare environments, and public service areas, contributing to inclusive communication and assistive technology development.
KEYWORDS
In conclusion, the AI-based gesture tool for disabled persons is a simple, effective, and user-friendly solution to improve communication between impaired people and society. The system uses artificial intelligence and computer vision to recognize hand movements and translate them into real-time actions or messages. This builds confidence, independence, and lessens dependence. The proposed system is cost-effective, scalable, and customizable with speech output, language support, and mobile or IoT integration, making it a useful assistive tool for inclusive communication.
[1] Starner, T., Pentland, A., & Weiser, M. (1998). Real-time American sign language recognition from video using hidden Markov models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(12), 1371-1375. [DOI: 10.1109/34.735807] [2] Huenerfauth, M., Kuber, R., & Savage, J. (2013). Signglasses: A sign language translation system using mobile devices. In Proceedings of the 15th International ACM SIGACCESS Conference on Computers and Accessibility (pp. 1-8). ACM. [DOI: 10.1145/2513383.2513442] [3] Sutton-Spence, R., & Woll, B. (1999). The Linguistics of British Sign Language: An Introduction. Cambridge University Press. [4] Camgoz, N. C., Hadfield, S., Koller, O., & Bowden, R. (2018). Neural sign language translation. Computer Vision and Image Understanding, 169, 150-159. [DOI: 10.1016/j.cviu.2018.01.006] [5] World Wide Web Consortium (W3C). (2018). Web Content Accessibility Guidelines (WCAG) 2.1. Retrieved from https://www.w3.org/TR/WCAG21/ [6] Stoll, C., Nickchen, T., Aein, M. J., & Cremers, D. (2020). Sign language recognition using 3D neural networks. arXiv preprint arXiv:2003.09011.
Publication Date
5/24/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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The authors declare no conflict of interest related to this research work.
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