Two-stage segmentation of unconstrained handwritten Chinese characters

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

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Detail(s)

Original languageEnglish
Pages (from-to)145-156
Journal / PublicationPattern Recognition
Volume36
Issue number1
Publication statusPublished - Jan 2003

Abstract

Correct segmentation of handwritten Chinese characters is crucial to their successful recognition. However, due to many difficulties involved, little work has been reported in this area. In this paper, a two-stage approach is presented to segment unconstrained handwritten Chinese characters. A handwritten Chinese character string is first coarsely segmented according to the background skeleton and vertical projection after a proper image preprocessing. With several geometric features, all possible segmentation paths are evaluated by using the fuzzy decision rules learned from examples. As a result, unsuitable segmentation paths are discarded. In the fine segmentation stage that follows, the strokes that may contain segmentation points are first identified. The feature points are then extracted from candidate strokes and taken as segmentation point candidates through each of which a segmentation path may be formed. The geometric features similar to the coarse segmentation stage are used and corresponding fuzzy decision rules are generated to evaluate fine segmentation paths. Experimental results on 1000 Chinese character strings from postal mail show that our approach can achieve a reasonable good overall accuracy in segmenting unconstrained handwritten Chinese characters. © 2002 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.

Research Area(s)

  • Character segmentation, Chinese character recognition, Decision trees, Fuzzy decision rules, Image preprocessing, Unconstrained handwritten Chinese characters

Citation Format(s)

Two-stage segmentation of unconstrained handwritten Chinese characters. / Zhao, Shuyan; Chi, Zheru; Shi, Penfei et al.
In: Pattern Recognition, Vol. 36, No. 1, 01.2003, p. 145-156.

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review