Journal of the Korean Society for Industrial and Applied Mathematics

Korean Society for Industrial and Applied Mathematics (한국산업응용수학회)
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HANDWRITTEN HANGUL RECOGNITION MODEL USING MULTI-LABEL CLASSIFICATION

  • HANA CHOI (DEPARTMENT OF INNOVATION CENTER FOR INDUSTRIAL MATHEMATICS, NATIONAL INSTITUTE FOR MATHEMATICAL SCIENCES)
  • Received : 2022.11.30
  • Accepted : 2023.04.02
  • Published : 2023.06.25
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Abstract

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

Keywords

Acknowledgement

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

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