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Visual word density-based nonlinear shape normalization method for handwritten Chinese character recognition

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Abstract

In handwritten Chinese character recognition, the performance of a system is largely dependent on the character normalization method. In this paper, a visual word density-based nonlinear normalization method is proposed for handwritten Chinese character recognition. The underlying rationality is that the density for each image pixel should be determined by the visual word around this pixel. Visual vocabulary is used for mapping from a visual word to a density value. The mapping vocabulary is learned to maximize the ratio of the between-class variation and the within-class variation. Feature extraction is involved in the optimization stage, hence the proposed normalization method is beneficial for the following feature extraction. Furthermore, the proposed method can be applied to some other image classification problems in which scene character recognition is tried in this paper. Experimental results on one constrained handwriting database (CASIA) and one unconstrained handwriting database (CASIA-HWDB1.1) demonstrate that the proposed method outperforms the start-of-the-art methods. Experiments on scene character databases chars74k and ICDAR03-CH show that the proposed method is promising for some image classification problems.

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Acknowledgments

We would like to express our sincere appreciation to the anonymous reviewers for their insightful comments, which have greatly aided us in improving the quality of the paper. This work was supported by National Natural Science Foundation of China (60933010, 61172103, 60835001).

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Authors and Affiliations

  1. Institute of Automation Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing, 100190, China

    Yunxue Shao, Chunheng Wang & Baihua Xiao

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  1. Yunxue Shao
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  2. Chunheng Wang
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  3. Baihua Xiao
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Correspondence to Chunheng Wang.

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Shao, Y., Wang, C. & Xiao, B. Visual word density-based nonlinear shape normalization method for handwritten Chinese character recognition. IJDAR 16, 387–397 (2013). https://doi.org/10.1007/s10032-012-0198-4

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  • DOI: https://doi.org/10.1007/s10032-012-0198-4

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