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Stream/Area:
-
Subject:
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Paper ID:
IJSREM68517
Volume:
10 |
10 |
Oct 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.
Artificial Intelligence
Zeal Polytechnic,Pune
Artificial Intelligence
Zeal Polytechnic,Pune
Artificial Intelligence
Zeal Polytechnic,Pune
Artificial Intelligence
Zeal Polytechnic,Pune
Artificial Intelligence
Zeal Polytechnic,Pune
Communication between people who use sign language and people who do not understand it can be difficult when a qualified interpreter is unavailable. This paper proposes a real-time, camera-based sign language translation system designed to support two-way interaction. In the sign-to-language direction, video frames are processed to detect hands and relevant movement, a recognition model classifies a supported sign or sign sequence, and the result is presented as text with optional speech synthesis. In the reverse direction, typed text or recognized speech is processed and mapped to a sign-language-specific visual representation, such as a verified sign clip or an articulated avatar. The design separates hand tracking, recognition, language processing, and output modules so that components can be tested and replaced independently. MediaPipe-style landmark extraction and machine-learning classifiers are considered for a constrained prototype, while continuous sign-language translation requires temporal modeling and linguistic context. The paper describes the architecture, methodology, evaluation plan, applications, limitations, and future development. No experimental performance is claimed because implementation details, dataset measurements, and test results were not supplied. The proposed system is therefore presented as a research and implementation framework rather than a validated product.
KEYWORDS
This paper has described a proposed real-time sign language translation framework for two-way communication. Its sign-totext/speech pipeline combines camera input, landmark-based feature extraction, sign classification, text generation, and optional speech synthesis. Its reverse pipeline accepts text or speech and produces a target-language sign representation using validated clips or a future animation module. The design separates recognition from translation and avoids claiming that isolated gesture classification or word lookup provides unrestricted natural sign language. Since no implementation measurements were supplied, empirical performance remains to be established through a signer-independent evaluation, latency testing, robustness analysis, and review by sign-language users. With language-specific data and careful validation, the system could become a useful assistive communication prototype.
[1] N. C. Camgoz, S. Hadfield, O. Koller, H. Ney, and R. Bowden, “Neural sign language translation,” in Proc. IEEE/CVF Conf. Computer Vision and Pattern Recognition (CVPR), 2018, pp. 7784–7793. [2] “Sign language translation: A survey of approaches and techniques,” Electronics, vol. 12, no. 12, art. 2678, 2023, doi: 10.3390/electronics12122678. [3] D. Li, C. Rodriguez, X. Yu, and H. Li, “Word-level deep sign language recognition from video: A new large-scale dataset and methods comparison,” in Proc. IEEE/CVF Winter Conf. Applications of Computer Vision (WACV), 2020, pp. 1459–1469. [4] F. Zhang, V. Bazarevsky, A. Vakunov, A. Tkachenka, G. Sung, C.-L. Chang, and M. Grundmann, “MediaPipe Hands: On-device real-time hand tracking,” arXiv:2006.10214, 2020. [5] S. Stoll, N. C. Camgoz, S. Hadfield, and R. Bowden, “Text2Sign: Towards sign language production using neural machine translation and generative adversarial networks,” Int. J. Comput. Vis., vol. 128, pp. 891– 908, 2020, doi: 10.1007/s11263-019-01281-2. [6] A. Wali, R. Shariq, S. Shoaib, S. Amir, and A. A. Farhan, “Recent progress in sign language recognition: A review,” Machine Vision and Applications, vol. 34, art. 127, 2023, doi: 10.1007/s00138-023-01479-y.
Publication Date
10/30/2026
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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