Indian Hand Sign Gesture Recognition using Deep Learning : Bridging the Communication Gap with AI-Powered Sign Detection
Keywords:
Gesture, AI-Powered, Deep LearningAbstract
Hand sign gesture recognition is the process of a system to recognize human gestures through mathematical algorithms. Computer vision and machine learning are generally involved. Consider the applications in human-computer interaction, sign language translation, virtual reality, and robotics. The paper discussed the sign language work in the vision-based hand gesture recognition system for the period 2014 to 2020. The aim here is to determine the progress and what demands more focus. We have retrieved a total of 98 articles from prominent online databases using chosen keywords. The review indicates that the vision-based hand gesture recognition research is an ongoing area of research, and numerous studies have been carried out, leading to dozens of papers being published each year in conferences and journals. The majority of the papers are centered on three important areas of the vision-based hand gesture recognition system, which are: data acquisition, data environment, and hand gesture representation. We also discussed the performance of vision-based hand gesture recognition system based on recognition accuracy. In the case of signer dependent, recognition accuracy.
Downloads
References
REFERENCES
[1] P. K. Pisha Rady and M. Saerbeck, Recent methods and databases in vision-based hand gesture recognition: A review, Comput.Vis. Image Understand., vol. 141, pp. 152165, Dec. 2015, doi: 10.1016/j.cviu.2015.08.004.
[2] M.Yasen and S.Jusoh, A systematic review on hand gesture recognition techniques, challenges and applications, Peer J Comput. Sci., vol. 5, p. e218, Sep. 2019.
[3] M. J. Cheok, Z. Omar, and M. H. Jaward, A review of hand gesture and sign language recognition techniques, Int. J. Mach. Learn. Cybern., vol. 10, no. 1, pp. 131153, Jan. 2017, doi: 10.1007/s13042- 017-0705-5.
[4] S. Kausar and M. Y. Javed,A survey on sign language recognition, in Proc. Frontiers Inf. Technol., 2011, pp. 9598.
[5] H. Cooper, B. Holt, and R. Bowden, Sign language recognition, in Visual Analysis of Humans. London, U.K.: Springer, 2011, pp. 539562.
[6] G. Fang, W. Gao, and D. Zhao,Largevocabularysign language recognition basedon fuzzydecision trees, IEEE Trans. Syst., Man, Cybern. A, Syst. Humans, vol. 34, no. 3, pp. 305314, May 2004. [7] M.Mohandes, M.Deriche, U. Johar, and S. Ilyas, A signer-independent Arabic sign language recognition system using face detection, geometric features, and a hidden Markov model, Comput. Electr. Eng., vol. 38, no. 2, pp. 422433, 2012.
[8] S. C. W. Ong, S. Ranganath, and Y.V. Venkatesh,Understanding gestureswith systematicvariations in movement dynamics, Pattern Recognit., vol. 39, no. 9, pp. 16331648, Sep. 2006.
[9] B. K. Chakraborty, D. Sarma, M. K. Bhuyan, and K. F. Mac Dorman, Review of constraints on vision-based gesture recognition for human computer interaction, IET Comput. Vis., vol. 12, no. 1, pp. 315, Feb. 2018, doi: 10.1049/iet-cvi.2017.0052.
[10] S. S. Rautaray and A. Agrawal, Vision based hand gesture recognition for human computer interaction: A survey, Artif. Intell. Rev., vol. 43, no. 1, pp. 154, Jan. 2012, doi: 10.1007/s10462-012- 9356-9.
[11] M. A. Moni and A. B. M.S.Ali, HMM based handgesture recognition: Areview on techniques and approaches, in Proc. 2nd IEEE Int. Conf. Comput. Sci. Inf. Technol., 2009, pp. 433437.
[12] A. Wadhawan and P. Kumar, Sign language recognition systems: A decade systematic literature review, Arch. Comput. Methods Eng., vol. 28, pp. 785813, May 2021, doi: 10.1007/s11831 -019-09384-
Downloads
Published
Versions
- 09-04-2025 (2)
- 22-03-2025 (1)
Issue
Section
License
Copyright (c) 2025 International Journal for Research Publication and Seminar
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
