상세정보
학술대회
Classification of machine-printed and handwritten addresses on Korean mail piece images using geometric features
- 발행일
- 200408
- 출처
- International Conference on Pattern Recognition (ICPR) 2004, pp.1-4
- 협약과제
- 04ME1200, uPost 기반 기술 개발, 김진석
- 초록
- In this paper, we propose an effective method for classifying machine-printed and handwritten addresses on Korean mail piece images. It is of vital importance to know if an input image is machine-printed or handwritten in such applications as address reading, form processing, FAX routing, and etc., since approaches for handwritten images are developed quite differently from those for machine-printed images. Our method consists of three blocks: valid connected component grouping, feature extraction and classification. A set of features related to width and position of groups of valid connected components is used for the classification based on a multi-layer perceptrons network. The experiment done with address images extracted from Korean live mail piece images has demonstrated the superiority of the proposed method. The correct classification rate for 3,147 testing images was about 98.9%.
- KSP 제안 키워드
- Feature Extraction and Classification, Geometric features, Machine-printed, connected component, correct classification rate(CCR), multilayer perceptron