KIISE Transactions on Computing Practices (정보과학회 컴퓨팅의 실제 논문지)

Korean Institute of Information Scientists and Engineers (한국정보과학회)
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Hangul Handwriting Recognition using Recurrent Neural Networks

순환신경망을 이용한 한글 필기체 인식

  • Received : 2016.10.10
  • Accepted : 2017.02.14
  • Published : 2017.05.15
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Abstract

We analyze the online Hangul handwriting recognition problem (HHR) and present solutions based on recurrent neural networks. The solutions are organized according to the three kinds of sequence labeling problem - sequence classifications, segment classification, and temporal classification, with additional consideration of the structural constitution of Hangul characters. We present a stacked gated recurrent unit (GRU) based model as the natural HHR solution in the sequence classification level. The proposed model shows 86.2% accuracy for recognizing 2350 Hangul characters and 98.2% accuracy for recognizing the six types of Hangul characters. We show that the type recognizing model successfully follows the type change as strokes are sequentially written. These results show the potential for RNN models to learn high-level structural information from sequential data.

온라인 방식의 한글 필기체 인식 문제를 분석하고 순환신경망 기반의 해법을 모색한다. 한글 낱글자 인식 문제를 순서데이터 레이블링의 관점에서 서열 분류, 구간 분류, 시간별 분류의 세 단계로 구분하여 각각에 대한 해법을 살펴보며, 한글의 구성 원리를 고려한 해결 방안을 정리한다. 한글 2350글자에 대한 온라인 필기체 데이터에 GRU(gated recurrent unit)의 다층 구조를 가지는 서열 분류모델을 적용한 결과, 낱글자 인식 정확도는 86.2%, 초 중 종성 구성에 따른 6가지 유형 분류 정확도는 98.2%로 측정되었다. 유형 분류 모델로 획의 진행에 따른 유형 변화 역시 높은 정확도로 인식하는 결과를 통해, 순환신경망을 이용하여 순서 데이터에서 한글의 구조와 같은 고차원적 지식을 학습할 수 있음을 확인하였다.

Keywords

Acknowledgement

Supported by : 정보통신기술진흥센터, 한국산업기술평가관리원

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