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

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dc.citedby0
dc.contributor.authorMukhanov S.en_US
dc.contributor.authorUskenbayeva R.en_US
dc.contributor.authorRakhim A.A.en_US
dc.contributor.authorAkim A.en_US
dc.contributor.authorMamanova S.en_US
dc.contributor.authorid57209659807en_US
dc.contributor.authorid55623134100en_US
dc.contributor.authorid59333012900en_US
dc.contributor.authorid59332938100en_US
dc.contributor.authorid59333088300en_US
dc.date.accessioned2025-03-03T07:46:41Z
dc.date.available2025-03-03T07:46:41Z
dc.date.issued2024
dc.description.abstractCurrently, 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.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1016/j.procs.2024.08.064
dc.identifier.epage463
dc.identifier.scopus2-s2.0-85204295071
dc.identifier.spage458
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85204295071&doi=10.1016%2fj.procs.2024.08.064&partnerID=40&md5=69e60dec61afcbec41eb507af7b41aa0
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/37020
dc.identifier.volume241
dc.pagecount5
dc.publisherElsevier B.V.en_US
dc.relation.ispartofAll Open Access; Gold Open Access
dc.sourceScopus
dc.sourcetitleProcedia Computer Science
dc.subjectAdversarial machine learning
dc.subjectContrastive Learning
dc.subjectDeep neural networks
dc.subjectGesture recognition
dc.subjectGluing
dc.subjectLong short-term memory
dc.subjectPalmprint recognition
dc.subjectSelf-supervised learning
dc.subjectSupport vector machines
dc.subjectUnsupervised learning
dc.subjectGestures recognition
dc.subjectHand gesture
dc.subjectHand gesture recogtion
dc.subjectKazakh sign language
dc.subjectLearning models
dc.subjectNeural netwrok
dc.subjectNeural-networks
dc.subjectShort term memory
dc.subjectSign language
dc.subjectSupport vectors machine
dc.subjectConvolutional neural networks
dc.titleGesture recognition of the Kazakh alphabet based on machine and deep learning modelsen_US
dc.typeConference paperen_US
dspace.entity.typePublication
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