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Learning confidence transformation for handwritten Chinese text recognition

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Abstract

Handwritten text recognition systems commonly combine character classification confidence scores and context models for evaluating candidate segmentation-recognition paths, and the classification confidence is usually optimized at character level. In this paper, we investigate into different confidence-learning methods for handwritten Chinese text recognition and propose a string-level confidence-learning method, which estimates confidence parameters by directly optimizing the performance of character string recognition. We first compare the performances of parametric (class-dependent and class-independent parameters) and nonparametric (isotonic regression) confidence-learning methods. Then, we propose two regularized confidence estimation methods and particularly, a string-level confidence-learning method under the minimum classification error criterion. In experiments of online handwritten Chinese text recognition, the string-level confidence-learning method is shown to effectively improve the string recognition performance. Using three character classifiers, the character correct rates are improved from 92.39, 90.24 and 88.69 % to 92.76, 90.91 and 89.93 %, respectively.

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Acknowledgments

This work was supported by the National Natural Science Foundation of China (NSFC) Grant 60933010. The authors would like to thank Xu-Yao Zhang for helpful discussions. The work of Da-Han Wang was partly accomplished at the Institute of Automation of Chinese Academy of Sciences.

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Authors and Affiliations

  1. Center for Pattern Analysis and Machine Intelligence, School of Information Science and Engineering, Xiamen University, Fujian, 361005, China

    Da-Han Wang

  2. National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguan East Road, Beijing, 100190, China

    Cheng-Lin Liu

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  1. Da-Han Wang
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  2. Cheng-Lin Liu
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Correspondence to Cheng-Lin Liu.

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Wang, DH., Liu, CL. Learning confidence transformation for handwritten Chinese text recognition. IJDAR 17, 205–219 (2014). https://doi.org/10.1007/s10032-013-0214-3

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  • DOI: https://doi.org/10.1007/s10032-013-0214-3

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