Journal of the Korea Institute of Information and Communication Engineering (한국정보통신학회논문지)

The Korea Institute of Information and Commucation Engineering (한국정보통신학회)
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Recognition of Handwritten Numerals using Eigenvectors

고유벡터를 이용한 필기체 숫자인식

  • 박중조 (경상대학교 전자전기공학부,컴퓨터정보통신연구소) ;
  • 김경민 (컴퓨터 전자통신 및 전기공학부) ;
  • 송명현 (순천대학교 정보통신공학부)
  • Published : 2002.10.01
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Abstract

This paper presents off-line handwritten numeral recognition method by using Eigen-Vectors. In this method, numeral features are extracted statistically by using Eigen-Vectors through KL transform and input numeral is recognized in the feature space by the nearest-neighbor classifier. In our feature extraction method, basis vectors which express best the property of each numeral type within the extensive database of sample numeral images are calculated, and the numeral features are obtained by using this basis vectors. Through the experiments with the unconstrained handwritten numeral database of Concordia University, we have achieved a recognition rate of 96.2%.

본 논문에서는 고유벡터를 이용한 오프라인 필기체 숫자인식 기법을 제시한다. 본 기법에서는 KL 변환에 의한 고유벡터를 이용하여 통계적으로 숫자의 특징을 추출하며, 특징공간상에서 최소거리기법으로 숫자를 인식한다. 본 기법에서 제안된 특징추출 방법에서는 많은 표본 숫자영상에서 각 숫자들의 특징을 가장 잘 표현하는 기저벡터를 찾아내고 이로부터 숫자의 특징을 구한다. 제시된 기법의 성능 평가를 위해 Concordia대학의 무제약 필기체 숫자 데이터베이스를 사용하여 실험한 결과 96.2%의 인식률을 얻을 수 있었다.

Keywords

References

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