Journal of the Korean Society for Industrial and Applied Mathematics

  • 제27권2호
  • /
  • Pages.135-145
  • /
  • 2023
  • /
  • 1226-9433(pISSN)
  • /
  • 1229-0645(eISSN)
한국산업응용수학회 (Korean Society for Industrial and Applied Mathematics)
권호 표지
DOI QR코드

DOI QR Code

HANDWRITTEN HANGUL RECOGNITION MODEL USING MULTI-LABEL CLASSIFICATION

  • HANA CHOI (DEPARTMENT OF INNOVATION CENTER FOR INDUSTRIAL MATHEMATICS, NATIONAL INSTITUTE FOR MATHEMATICAL SCIENCES)
  • 투고 : 2022.11.30
  • 심사 : 2023.04.02
  • 발행 : 2023.06.25
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

Recently, as deep learning technology has developed, various deep learning technologies have been introduced in handwritten recognition, greatly contributing to performance improvement. The recognition accuracy of handwritten Hangeul recognition has also improved significantly, but prior research has focused on recognizing 520 Hangul characters or 2,350 Hangul characters using SERI95 data or PE92 data. In the past, most of the expressions were possible with 2,350 Hangul characters, but as globalization progresses and information and communication technology develops, there are many cases where various foreign words need to be expressed in Hangul. In this paper, we propose a model that recognizes and combines the consonants, medial vowels, and final consonants of a Korean syllable using a multi-label classification model, and achieves a high recognition accuracy of 98.38% as a result of learning with the public data of Korean handwritten characters, PE92. In addition, this model learned only 2,350 Hangul characters, but can recognize the characters which is not included in the 2,350 Hangul characters

키워드

과제정보

The work of H. Choi was supported by National Institute for Mathematical Sciences(NIMS) grant funded by the Korea government( MSIT ) No.B22810000.

참고문헌

  1. D. H. Kim and S. Y. Bang, An Overview of Hangul Handwritten Image Database PE92, Proceedings of Annual Conference on Human and Language Technology, 1992. 
  2. CLOVA OCR, NAVER Cloud Platform, accessed July 19, 2022, https://clova.ai/ocr. 
  3. I. Kim and X. Xie, Handwritten Hangul recognition using deep convolutional neural networks, International Journal on Document Analysis and Recognition (IJDAR), 19 (2015), 1-13. 
  4. I. Kim, C. Choi and S. Lee, Improving discrimination ability of convolutional neural networks by hybrid learning, International Journal on Document Analysis and Recognition (IJDAR), 19 (2016), 1-9.  https://doi.org/10.1007/s10032-015-0256-9
  5. H. Kim and Y. Chung, Improved Handwritten Hangeul Recognition using Deep Learning based on GoogLenet, Journal of the Korea Contents Association, 18 (2018), 495-502.  https://doi.org/10.5392/JKCA.2018.18.07.495
  6. H. Choi, Applications of Deep Convolutional Neural Networks: Enhanced Handwritten Hangul Recognition Model, Diss. Sungkyunkwan Univ, (2020), Print. 
  7. J. Bogatinovski, L. Todorovski, S. Dzeroski and D. Kocev, Comprehensive comparative study of multi-label classification methods, Expert Systems with Applications, 203 (2022). 
  8. J. Xu, J. Liu, J. Yin and C. Sun, A multi-label feature extraction algorithm via maximizing feature variance and feature-label dependence simultaneously, Knowledge-Based Systems, 19 (2016), 192-84. 
  9. F. Briggs, B. Lakshminarayanan, L. Neal, X. Z. Fern, R. Raich, S. J. K. Hadley, A.S. Hadley and M. G. Betts, Acoustic classification of multiple simultaneous bird species: A multi-instance multi-label approach, Journal of the Acoustical Society of America, 131 (2012), 4640-4650.  https://doi.org/10.1121/1.4707424
  10. K. He, X. Zhang, S. Ren and J. Sun, Deep Residual Learning for Image Recognition, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition(CVPR), NV, USA 2016.