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

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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.

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Acknowledgments

This work was supported by the National Natural Science Foundation of China (NSFC) grants 60933010 and 61175021. The authors thank Prof. Horst Bunke, Dr. TongHua Su, Yan-Wei Wang and Xu-Yao Zhang for helpful discussions.

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Authors and Affiliations

  1. National Laboratory of Pattern Recognition, Institute of Automation of Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing, 100190, People’s Republic of China

    Liang Xu, Fei Yin, Qiu-Feng Wang & Cheng-Lin Liu

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  1. Liang Xu
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  2. Fei Yin
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  3. Qiu-Feng Wang
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  4. Cheng-Lin Liu
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Correspondence to Liang Xu.

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Xu, L., Yin, F., Wang, QF. et al. An over-segmentation method for single-touching Chinese handwriting with learning-based filtering. IJDAR 17, 91–104 (2014). https://doi.org/10.1007/s10032-013-0208-1

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  • DOI: https://doi.org/10.1007/s10032-013-0208-1

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