Gesture recognition of the Kazakh alphabet based on machine and deep learning models

• Main Authors: Mukhanov S., Uskenbayeva R., Rakhim A.A., Akim A., Mamanova S.
• Other Authors: 57209659807
• Format: Conference paper
• Published: Elsevier B.V. 2025
• Subjects: Adversarial machine learning; Contrastive Learning; Deep neural networks; Gesture recognition; Gluing; Long short-term memory; Palmprint recognition; Self-supervised learning; Support vector machines; Unsupervised learning; Gestures recognition; Hand gesture; Hand gesture recogtion; Kazakh sign language; Learning models; Neural netwrok; Neural-networks; Short term memory; Sign language; Support vectors machine; Convolutional neural networks

Currently, a growing body of research focuses on addressing problems using computer vision libraries and artificial intelligence tools. The predominant approaches involve employing machine and deep learning models of artificial neural networks to recognize gestures in the Kazakh Sign Alphabet (KSA) via supervised and deep learning techniques for sequential data processing. Pattern recognition in this context involves identifying an object within an image, where the object can be abstract and vary in shape. We have chosen to investigate the field of gesture recognition, specifically. For recognizing Kazakh Sign Language (KSL), the initial step involves mastering the KSA. Training a neural network to recognize KSL necessitates the collection of datasets in the form of images depicting hand gestures. In this research, prominent hand gesture recognition models such as the Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Support Vector Machine (SVM) were analyzed. These models differ in their methodologies, processing times, and training data requirements. A significant aspect of this study is the application of unsupervised and supervised learning techniques including CNN, LSTM, and SVM. The experiments yielded diverse results when training neural networks for recognizing gestures in Kazakh sign language based on the dactyl alphabet. This article provides a comprehensive overview of each method, their specific purposes, and their effectiveness in terms of performance and training. Numerous experimental outcomes were documented in a table, showcasing the accuracy of recognizing each gesture. Additionally, specific hand gestures were tested in front of a camera to identify the gesture and display the result on the screen. A notable feature was the use of mathematical formulas and functions to elucidate the operating principles of the machine learning methods, as well as the logical structure and design of the LSTM model. ? 2024 The Authors. Published by Elsevier B.V.