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Atypical Character Recognition Based on Mask R-CNN for Hangul Signboard

  • Received : 2019.07.31
  • Accepted : 2019.08.12
  • Published : 2019.09.30

Abstract

This study proposes a method of learning and recognizing the characteristics that are the classification criteria of Hangul using Mask R-CNN, one of the deep learning techniques, to recognize and classify atypical Hangul characters. The atypical characters on the Hangul signboard have a lot of deformed and colorful shapes beyond the general characters. Therefore, in order to recognize the Hangul signboard character, it is necessary to learn a separate atypical Hangul character rather than the existing formulaic one. We selected the Hangul character '닭' as sample data and constructed 5,383 Hangul image data sets and used them for learning and verifying the deep learning model. The accuracy of the results of analyzing the performance of the learning model using the test set constructed to verify the reliability of the learning model was about 92.65% (the area detection rate). Therefore we confirmed that the proposed method is very useful for Hangul signboard character recognition, and we plan to extend it to various Hangul data.

Keywords

Atypical Character Recognition;Hangul Signboard;Mask R-CNN;Hangul signboard character

Acknowledgement

Supported by : Dong Yang University

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