한국지능시스템학회논문지 (Journal of the Korean Institute of Intelligent Systems)

  • 제11권3호
  • /
  • Pages.200-207
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  • 2001
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  • 1976-9172(pISSN)
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  • 2288-2324(eISSN)
한국지능시스템학회 (Korean Institute of Intelligent Systems)
권호 표지

분할기반 은닉 마르코프 모델과 다층 퍼셉트론 결합 영문수표필기단어 인식시스템

A Segmentation-Based HMM and MLP Hybrid Classifier for English Legal Word Recognition

  • 김계경 (한국전자통신연구원 영상처리연구부) ;
  • 김진호 (경일대학교 전자정보공학과) ;
  • 박희주 (경일대학교 컴퓨터공학과)
  • 발행 : 2001.06.01
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초록

본 논문에서는 분할기반 은닉 마르코프 모델(segmentation based hidden Markov model)과 다층 퍼셉트론 (multi-layer perceptron)을 결합한 영문수표 필기단어 (legal word) 인식시스템을 제안하였다. 가변길이의 필기체 영문 단어 분할결과를 인식할 수 있도록 은닉 마르코프 모델을 이용하여 명확한 분할기반 (explicit segmentation-based) 단어단위 (word level) 인식기를 구현하고 다층 퍼셉트론을 이용하여 내재적 분할기반 (implicit segmentation-based) 단어단위 인식기를 구현하였다. 그리고 이종(heterogeneous)의 두 인식기를 새로운 결합 확률추정방식에 따라 결합함으로서 상호 보완 능력을 극대화시킬 수 있는 영문수표 필기단어 인식시스템을 구현하였다. 제안한 시스템을 캐나다 콘코디아 대학의 CENPARMI 영문 수표 데이터베이스에 적용하여 실험해 본 결과 기존의 연구결과에 비해 비교적 우수한 인식성능을 얻을 수 있었다.

In this paper, we propose an HMM(Hidden Markov modeJ)-MLP(Multi-layer perceptron) hybrid model for recognizing legal words on the English bank check. We adopt an explicit segmentation-based word level architecture to implement an HMM engine with nonscaled and non-normalized symbol vectors. We also introduce an MLP for implicit segmentation-based word recognition. The final recognition model consists of a hybrid combination of the HMM and MLP with a new hybrid probability measure. The main contributions of this model are a novel design of the segmentation-based variable length HMMs and an efficient method of combining two heterogeneous recognition engines. ExperimenLs have been conducted using the legal word database of CENPARMI with encouraging results.

키워드

참고문헌

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