Abstract
In the course of our research work, the American, Russian and Turkish sign languages were analyzed. The program of recognition of the Kazakh dactylic sign language with the use of machine learning methods is implemented. A dataset of 5000 images was formed for each gesture, gesture recognition algorithms were applied, such as Random Forest, Support Vector Machine, Extreme Gradient Boosting, while two data types were combined into one database, which caused a change in the architecture of the system as a whole. The quality of the algorithms was also evaluated. The research work was carried out due to the fact that scientific work in the field of developing a system for recognizing the Kazakh language of sign dactyls is currently insufficient for a complete representation of the language. There are specific letters in the Kazakh language, because of the peculiarities of the spelling of the language, problems arise when developing recognition systems for the Kazakh sign language. The results of the work showed that the Support Vector Machine and Extreme Gradient Boosting algorithms are superior in real-time performance, but the Random Forest algorithm has high recognition accuracy. As a result, the accuracy of the classification algorithms was 98.86 % for Random Forest, 98.68 % for Support Vector Machine and 98.54 % for Extreme Gradient Boosting. Also, the evaluation of the quality of the work of classical algorithms has high indicators. The practical significance of this work lies in the fact that scientific research in the field of gesture recognition with the updated alphabet of the Kazakh language has not yet been conducted and the results of this work can be used by other researchers to conduct further research related to the recognition of the Kazakh dactyl sign language, as well as by researchers, engaged in the development of the international sign language
Highlights
A major advance in the field of information technology over the past ten years can be called the digitalization of human-computer interaction at the visual level
The presented research work is aimed at the correct recognition of the Kazakh sign language
The average accuracy of the Random Forest classifier was 98.86 %, the SVM algorithm showed 98.68 % accuracy, and XGBoost has a result of 98.54 % correct recognition
Summary
A major advance in the field of information technology over the past ten years can be called the digitalization of human-computer interaction at the visual level. This achievement primarily solves communication problems of people with hearing disabilities and allows for rapid human-computer interaction. The results of automatic gesture recognition and classification are used to train people with hearing impairments and help them communicate with strangers using sign language. They can be used as a quick message for digital smart devices. As video data has become ubiquitous in practical applications, the research and development of gesture recognition automation is finding application in many human-machine communication systems
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