KAZAKH HANDWRITING RECOGNITION
| dc.contributor.author | Bazarkulova A. | |
| dc.contributor.author | Mutalivev Y. | |
| dc.contributor.author | Chazhabayev A. | |
| dc.contributor.author | Telman D. | |
| dc.contributor.author | Bazarkulova D. | |
| dc.date.accessioned | 2024-01-04T10:41:10Z | |
| dc.date.available | 2024-01-04T10:41:10Z | |
| dc.date.issued | 2023 | |
| dc.description.abstract | 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. | |
| dc.identifier.citation | Aisaule Bazarkulova, Yelnur Mutalivev, Abylaikhan Chazhabayev , Duman Telman, Diana Bazarkulova / KAZAKH HANDWRITING RECOGNITION / СДУ хабаршысы - 2023 | |
| dc.identifier.issn | 2709-2631 | |
| dc.identifier.uri | https://repository.sdu.edu.kz/handle/123456789/1094 | |
| dc.language.iso | en | |
| dc.publisher | СДУ хабаршысы - 2023 | |
| dc.subject | recognition | |
| dc.subject | handwrite detection | |
| dc.subject | Kazakh language | |
| dc.subject | binarization method | |
| dc.subject | СДУ хабаршысы - 2023 | |
| dc.subject | №1 | |
| dc.title | KAZAKH HANDWRITING RECOGNITION | |
| dc.type | Article |