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- Vol. 7 Issue 2
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Sign Language recognition using Machine learning
Authors
1 2 3 Department of CSE, Sathyabama Institute of Science and Technology, Chennai, Tamil Nadu, India.
Article Information
- Published In
- Volume 7, Issue 2 (2026)
- Published Online
- March-April 2026
- Pages
- 145-151
- Article Type
- Original Article
References
1. A. K. Jain, P. Flynn and A. A. Ross, Handbook of Biometrics. New York, NY, USA: Springer, 2008.
2. S. M. Anwar and R. Qureshi, "Sign language recognition using deep learning," in Proc. Int. Conf. Frontiers of Information Technology
(FIT), Islamabad, Pakistan, Dec. 2018, pp. 147-151.
3. T. Starner and A. Pentland, "Real-time American Sign Language recognition from video using hidden Markov models," in Proc. Int.
Symp. Computer Vision, Coral Gables, FL, USA, Nov. 1995, pp. 265-270.
4. R. Rastgoo, K. Kiani and S. Escalera, "Sign language production: A review," Expert Syst. Appl., vol. 164, p. 113794, Feb. 2021
5. M. Kumar, D. Sharma, "Real-time sign language gesture recognition using CNN," in Proc. IEEE Int. Conf. Computing,
Communication and Automation (ICCCA), Greater Noida, India, Dec. 2020, pp. 567-572.
6. P. K. Pisharady, M. Saerbeck, Recent methods and databases in vision-based hand gesture recognition: A review, Comput. Vis. Image
Understand., vol. 141, pp. 152–165, Dec. 2015.
7. S. Sudha, A. Mahalakshmi, "Sign language recognition using machine learning techniques," in Proc. Int. Conf. Smart Technologies
and Management for Computing, Communicating, Controls, Energy and Materials (ICSM), Chennai, India, Aug. 2019, pp. 186 - 190.
8. G. R. K. S. Subramanian and P. Rajesh, "Vision-based Indian Sign Language recognition using convolutional neural networks," Int. J.
Eng. Technol., vol. 7, no. 3, pp. 112--118, Mar. 2018.
9. C. Keskin, F. Kiraq, Y. E. Kara, and L. Akarun, "Real time hand pose estimation using depth sensors," in Proc. IEEE Int. Conf.
Computer Vision Workshops (ICCVW), Barcelona, Spain, Nov. 2011, pp. 1228-1234.
10. S. S. Rautaray, A. Agrawal, Vision based hand gesture recognition for human computer interaction: A survey, Artif. Intell. Rev., vol.
43, pp. 1–54, Jan. 2015.
2. S. M. Anwar and R. Qureshi, "Sign language recognition using deep learning," in Proc. Int. Conf. Frontiers of Information Technology
(FIT), Islamabad, Pakistan, Dec. 2018, pp. 147-151.
3. T. Starner and A. Pentland, "Real-time American Sign Language recognition from video using hidden Markov models," in Proc. Int.
Symp. Computer Vision, Coral Gables, FL, USA, Nov. 1995, pp. 265-270.
4. R. Rastgoo, K. Kiani and S. Escalera, "Sign language production: A review," Expert Syst. Appl., vol. 164, p. 113794, Feb. 2021
5. M. Kumar, D. Sharma, "Real-time sign language gesture recognition using CNN," in Proc. IEEE Int. Conf. Computing,
Communication and Automation (ICCCA), Greater Noida, India, Dec. 2020, pp. 567-572.
6. P. K. Pisharady, M. Saerbeck, Recent methods and databases in vision-based hand gesture recognition: A review, Comput. Vis. Image
Understand., vol. 141, pp. 152–165, Dec. 2015.
7. S. Sudha, A. Mahalakshmi, "Sign language recognition using machine learning techniques," in Proc. Int. Conf. Smart Technologies
and Management for Computing, Communicating, Controls, Energy and Materials (ICSM), Chennai, India, Aug. 2019, pp. 186 - 190.
8. G. R. K. S. Subramanian and P. Rajesh, "Vision-based Indian Sign Language recognition using convolutional neural networks," Int. J.
Eng. Technol., vol. 7, no. 3, pp. 112--118, Mar. 2018.
9. C. Keskin, F. Kiraq, Y. E. Kara, and L. Akarun, "Real time hand pose estimation using depth sensors," in Proc. IEEE Int. Conf.
Computer Vision Workshops (ICCVW), Barcelona, Spain, Nov. 2011, pp. 1228-1234.
10. S. S. Rautaray, A. Agrawal, Vision based hand gesture recognition for human computer interaction: A survey, Artif. Intell. Rev., vol.
43, pp. 1–54, Jan. 2015.
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