Title: Through deep learning, dynamic hand gesture recognition of sign language learning algorithm

Authors: Tushar A. Champaneria; Harikrishna B. Jethva; S. Julia Faith; Neel Kumar Shrimali

Addresses: Computer Engineering Department, Government Engineering College, Modasa, Gujarat, 383315, India ' Computer Science and Engineering Department, Government Engineering College, Patan, Gujarat, 384265, India ' Department of Information Technology, S.A. Engineering College, Chennai, India ' Electronic and Communication Engineering Department, Ganpat University, Mehsana, Gujarat, 384001, India

Abstract: Speech is the most common way people talk to each other, but some people have trouble saying or hearing. In this study, a deep learning-based model is proposed that can figure out what words a person is trying to say from the way they move their hands. Deep learning models, like LSTM and GRU (feedback-based learning models), are used to figure out what signs are in Indian sign language (ISL) film clips that are not connected to each other. This study shows how machine learning methods can be used for real-time motion recognition in a wide range of human-computer interfaces. Experiments showed that the system could recognise hand postures with 99.4% accuracy and active gestures with an average accuracy of 93.72%. For datasets with easy backgrounds, the accuracy is almost 99%, for datasets with complicated backgrounds it is 92%, and for the video dataset it is 84%.

Keywords: deaf-mute people; human-machine contact; key frame extraction; inception deep-convolution network; video analytics.

DOI: 10.1504/IJCVR.2026.153128

International Journal of Computational Vision and Robotics, 2026 Vol.16 No.3, pp.295 - 308

Received: 08 Nov 2023
Accepted: 31 Dec 2023
Published online: 23 Apr 2026 *

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