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Learning confidence transformation for handwritten Chinese text recognition

  • Da-Han Wang
  • Cheng-Lin LiuEmail author
Original Paper

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.

Keywords

Handwritten text recognition Confidence learning Parametric and nonparametric Class-dependent and class-independent String-level learning 

Notes

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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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Center for Pattern Analysis and Machine Intelligence, School of Information Science and EngineeringXiamen UniversityFujianChina
  2. 2.National Laboratory of Pattern Recognition, Institute of AutomationChinese Academy of SciencesBeijingChina

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