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International Journal on Document Analysis and Recognition (IJDAR)
Erschienen in: International Journal on Document Analysis and Recognition (IJDAR) 1/2014

01.03.2014 | Original Paper

An over-segmentation method for single-touching Chinese handwriting with learning-based filtering

verfasst von: Liang Xu, Fei Yin, Qiu-Feng Wang, Cheng-Lin Liu

Erschienen in: International Journal on Document Analysis and Recognition (IJDAR) | Ausgabe 1/2014

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Abstract

The segmentation of touching characters is still a challenging task, posing a bottleneck for offline Chinese handwriting recognition. In this paper, we propose an effective over-segmentation method with learning-based filtering using geometric features for single-touching Chinese handwriting. First, we detect candidate cuts by skeleton and contour analysis to guarantee a high recall rate of character separation. A filter is designed by supervised learning and used to prune implausible cuts to improve the precision. Since the segmentation rules and features are independent of the string length, the proposed method can deal with touching strings with more than two characters. The proposed method is evaluated on both the character segmentation task and the text line recognition task. The results on two large databases demonstrate the superiority of the proposed method in dealing with single-touching Chinese handwriting.
Vorheriger Artikel Discrimination of similar characters using nonlinear normalization based on regional importance measure

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Metadaten
Titel
An over-segmentation method for single-touching Chinese handwriting with learning-based filtering
verfasst von
Liang Xu
Fei Yin
Qiu-Feng Wang
Cheng-Lin Liu
Publikationsdatum
01.03.2014
Verlag
Springer Berlin Heidelberg
Erschienen in
International Journal on Document Analysis and Recognition (IJDAR) / Ausgabe 1/2014
Print ISSN: 1433-2833
Elektronische ISSN: 1433-2825
DOI
https://doi.org/10.1007/s10032-013-0208-1

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