Hand gesture recognition using hybrid deep learning models for deaf communication support
Gautam Govindgovindgautam933@gmail.comDepartment of Information TechnologyManipal University JaipurJaipur, Rajasthan, 303007, IndiaView full profile → , Dhruv Guptadhruvagupta0706@gmail.comDepartment of Information TechnologyManipal University JaipurJaipur, Rajasthan, 303007, IndiaView full profile → , *Kavita JhajhariaCorresponding authorKavita.jhajharia@jaipur.manipal.eduDepartment of Information TechnologyManipal University JaipurJaipur, Rajasthan, 303007, IndiaView full profile →
* Corresponding author · click or hover a name for details
- Received:
- 01 Jun 2025
- Published Online:
- 27 Apr 2026
- Article type:
- Research Article
- Language:
- EN
- Article no.:
- JSMS-1584
- Pages:
- 529–552
Abstract
Keywords
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References
[1] S. S. Rautaray, “Real-time hand gesture recognition system for dynamic applications,” Int. J. UbiComp, vol. 3, pp. 21–31 (2012), doi: 10.5121/iju.2012.3103.
[2] N. H. Mohd Dhuzuki, A. A. Zainuddin, N. A. S. Kamarul Zaman, A. N. M. Ahmad Razmi, W. S. Kaitane, A. Ahmad Puzi, M. N. Johar, M. Yazid, N. A. Mohd Nordin, S. N. Sidek, and H. F. Mohd Zaki, “Design and implementation of a deep learning-based hand gesture recognition system for rehabilitation Internet-of-Things (RIoT) environments using MediaPipe,” IIUM Eng. J., vol. 26, no. 1, pp. 353–372 (Jan. 2025), doi: 10.31436/iiumej.v26i1.3455.
[3] R. P. Singh and L. D. Singh, “Dyhand: Dynamic hand gesture recognition using BiLSTM and soft attention methods,” Vis. Comput., vol. 41, pp. 41–51 (Jan. 2025), doi: 10.1007/s00371-024-03307-4.
[4] M. Linardakis, I. Varlamis, and G. Th. Papadopoulos, “Survey on hand gesture recognition from visual input,” arXiv:2501.11992 (2025), doi: 10.48550/arXiv.2501.11992.
[5] V.-H. Le, “Selected hand gesture recognition model based on cross-evaluation of deep learning from large RGB image datasets,” Multimedia Tools Appl., (Mar. 2025), doi: 10.1007/s11042-025-20743-z.
[6] J. Zhou, C. Xu, and L. Cheng, “Hand gesture recognition from an open-set perspective,” IEEE Trans. Multimedia, early access, pp. 1–12 (Jan. 2025), doi: 10.1109/TMM.2025.3535363.
[7] B. Suthar and S. I. Ali, “A survey on hand gesture sign language recognition with Arduino Leonardo,” in Proc. Int. Conf. Computational, Communication and Information Technology (ICCCIT), Indore, India (Feb. 2025), doi: 10.1109/ICCCIT62592.2025.10927994.
[8] Q. Chen, Q. Tang, M. Zhang, and L. Ma, “CNN-based gesture recognition using raw numerical gray-scale images of surface electromyography,” Biomed. Signal Process. Control, vol. 101, Art. no. 107176 (Mar. 2025), doi: 10.1016/j.bspc.2024.107176.
[9] M. Al-Hammadi, G. Muhammad, W. Abdul, M. Alsulaiman, M. A. Bencherif, and M. A. Mekhtiche, “Hand gesture recognition for sign language using 3DCNN,” IEEE Access, vol. 8, pp. 79491–79509 (Apr. 2020), doi: 10.1109/ACCESS.2020.2990434.
[10] J. Qi, L. Ma, Z. Cui, and Y. Yu, “Computer vision-based hand gesture recognition for human–robot interaction: A review,” Complex Intell. Syst., vol. 10, pp. 1581–1606 (2024), doi: 10.1007/s40747-023-01183-5.
[11] A. Mujahid, M. J. Awan, A. Yasin, M. A. Mohammed, R. Damaševičius, R. Maskeliūnas, and K. H. Abdulkareem, “Real-time hand gesture recognition based on deep learning YOLOv3 model,” Appl. Sci., vol. 11, no. 9, pp. 4164 (May 2021), doi: 10.3390/app11094164.
[12] M. A. Ozdemir, D. H. Kisa, O. Guren, A. Onan, and A. Akan, “EMG-based hand gesture recognition using deep learning,” in Proc. Med. Technologies Congress (TIPTEKNO), Antalya, Turkey, pp. 1–4 (Nov. 2020), doi: 10.1109/TIPTEKNO50054.2020.9299264.
[13] A. Kapitanov, K. Kvanchiani, A. Nagaev, R. Kraynov, and A. Makhliarchuk, “HaGRID—Hand gesture recognition image dataset,” in Proc. IEEE/CVF Winter Conf. Appl. Comput. Vis. (WACV), pp. 4572–4581 (2024).
