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Classification of Handwritten and Machine-printed Korean Address Image based on Connected Component Analysis  

장승익 (한국전자통신연구원 우정기술연구센터)
정선화 (한국전자통신연구원 우정기술연구센터)
임길택 (한국전자통신연구원 우정기술연구센터)
남윤석 (한국전자통신연구원 우정기술연구센터)
Publication Information
Journal of KIISE:Software and Applications / v.30, no.10, 2003 , pp. 904-911 More about this Journal
Abstract
In this paper, we propose an effective method for the distinction between machine-printed and handwritten Korean address images. It is important to know whether an input image is handwritten or machine-printed, because methods for handwritten image are quite different from those of machine-printed image in such applications as address reading, form processing, FAX routing, and so on. Our method consists of three blocks: valid connected components grouping, feature extraction, and classification. Features related to width and position of groups of valid connected components are used for the classification based on a neural network. The experiment done with live Korean address images has demonstrated the superiority of the proposed method. The correct classification rate for 3,147 testing images was about 98.85%.
Keywords
Optical character recognition (OCR); Address type identification; Address reading system; Multi-layer perceptrons;
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