Browsing by Author "Bazarkulova A."

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    Automatization of diploma processes based on analysis of supervisors’ datasets
    (2023) Bazarkulova A.
    The primary objective of the dissertation work was to extend and combine the hypothetical and useful information picked up in the learning procedure, and use them to research recommendation systems, in order to simplify the search for supervisors and students in areas of interest . To accomplish this objective, data was collected from different universities supervisors of Kazakhstan in the field of Information Technology. The object of study is to develop a system of recommendations based on the data of supervisors. As a result of the dissertation work, a research was conducted among clustering algorithms. The peculiarity and reliability of the obtained results is justified by the correlation between the rules of accepted standards of programming, consistency with the claims of and general the development environment and the technologies that were used.
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    KAZAKH HANDWRITING RECOGNITION
    (СДУ хабаршысы - 2023, 2023) Bazarkulova A.; Mutalivev Y.; Chazhabayev A.; Telman D. ; Bazarkulova D.
    Abstract. Recognition of handwritten text is one aspect of object recognition and known as handwriting detection cause of a computer’s potential to recognize and comprehend readable handwriting from resources including paper files, touch smart devices, images, etc. Data is categorized into a number of classes or groups using pattern recognition. The paper presents a successful experiment in recognizing handwritten Kazakh text using Convolutional Recurrent Neural Network based architectures and the Kazakh Autonomous Handwritten Text Dataset. The proposed algorithm achieved an overall accuracy of 86.36% and showed promising results. However, the paper suggests that further research could be conducted to improve the model, such as correlating and enlarging the database or incorporating other models and libraries. Additionally, the paper emphasizes the importance of considering language specifics when building a text recognition model, as modern algorithms that work well in one language may not guarantee the same performance in another.