Abstract
In this paper, a fast self-generation voting method is proposed for further improving the performance in handwritten Chinese character recognition. In this method, firstly, a set of samples are generated by the proposed fast self-generation method, and then these samples are classified by the baseline classifier, and the final recognition result is determined by voting from these classification results. Two methods that are normalization-cooperated feature extraction strategy and an approximated line density are used for speeding up the self-generation method. We evaluate the proposed method on the CASIA and CASIA-HWDB1.1 databases. High recognition rate of 98.84 % on the CASIA database and 91.17 % on the CASIA-HWDB1.1 database are obtained. These results demonstrate that the proposed method outperforms the state-of-the-art methods and is useful for practical applications.
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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 (61172103,60933010,60835001).
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Shao, Y., Wang, C. & Xiao, B. Fast self-generation voting for handwritten Chinese character recognition. IJDAR 16, 413–424 (2013). https://doi.org/10.1007/s10032-012-0194-8
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DOI: https://doi.org/10.1007/s10032-012-0194-8