• Journal of KIISE:Software and Applications
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• v.30 no.10
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• pp.904-911
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• 2003

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%.

• Journal of KIISE:Software and Applications
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• v.31 no.1
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• pp.8-19
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• 2004

In this paper, we propose an efficient method for locating destination address block on both of machine-Printed and handwritten Korean mail piece images. The proposed method extracts connected components from the binary mail piece image, generates text lines by merging them, and then groups the text fines into nine clusters. The destination address block is determined by selecting some clusters. Considering the geometric characteristics of address information on Korean mail piece, we split a mail piece image into nine areas with an equal size. The nine clusters are initialized with the center coordinate of each area. A modified Manhattan distance function is used to compute the distance between text lines and clusters. We modified the distance function on which the aspect ratio of mail piece could be reflected. The experiment done with live Korean mail piece images has demonstrated the superiority of the Proposed method. The success rate for 1, 988 testing images was about 93.56%.

• Proceedings of the IEEK Conference
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• 2002.07a
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• pp.192-195
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• 2002

About 90% of Thai-style addresses have similar features; e.g. the beginning of each address line is diagonal. In this paper, we propose a method for locating destination address block (DAB) on Thai envelopes based on features of Thai-style addresses. Firstly, we decompose image into smaller blocks and remove all blocks not meeting criteria. Secondly, we search for the DAB candidates. Lastly, heuristic rules and typical features are applied to identify the destination address block. Experimental results using 2,700 envelopes of handwritten and machine printed Thai envelopes show a successful address extraction rate of 91%.