Cost-Sensitive Transformation for Chinese Address Recognition

This paper proposes a cost-sensitive transformation for improving handwritten address recognition performance by converting a general-purpose handwritten Chinese character recognition engine to a special-purpose one. The class probabilities produced by character recognition engine for predicting a sample to candidate classes are transformed to the expected costs based on Naive Bayes optimal theoretical predictions firstly. And then candidate probabilities are reestimated based on the expected costs. Two general-purpose offline handwritten Chinese character recognition engines, PAIS and HAW, are tested in our experiments by applying them in handwritten Chinese address recognition system. 1822 live handwritten Chinese address images are tested with multiple cost matrices. Experimental results show that cost-sensitive transformation improves the recognition performance of general purpose recognition engines on handwritten Chinese address recognition.

[1]  Pedro M. Domingos.  MetaCost: a general method for making classifiers cost-sensitive , 1999, KDD '99.

[2]  Cheng-Lin Liu,et al.  Lexicon-Driven Segmentation and Recognition of Handwritten Character Strings for Japanese Address Reading , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  Fei Yin,et al.  CASIA Online and Offline Chinese Handwriting Databases , 2011, 2011 International Conference on Document Analysis and Recognition.

[4]  Zhi-Hua Zhou,et al.  Cost-sensitive face recognition , 2008, CVPR.

[5]  Zhi-Hua Zhou,et al.  Ieee Transactions on Knowledge and Data Engineering 1 Training Cost-sensitive Neural Networks with Methods Addressing the Class Imbalance Problem , 2022 .

[6]  Y. Jiang,et al.  A Suffix Tree Based Handwritten Chinese Address Recognition System , 2007, Ninth International Conference on Document Analysis and Recognition (ICDAR 2007).

[7]  Charles Elkan,et al.  The Foundations of Cost-Sensitive Learning , 2001, IJCAI.

[8]  Qiang Yang,et al.  Test-cost sensitive naive Bayes classification , 2004, Fourth IEEE International Conference on Data Mining (ICDM'04).

[9]  Peter D. Turney.  Cost-Sensitive Classification: Empirical Evaluation of a Hybrid Genetic Decision Tree Induction Algorithm , 1994, J. Artif. Intell. Res..

[10]  Victor S. Sheng,et al.  Thresholding for Making Classifiers Cost-sensitive , 2006, AAAI.

[11]  Qiang Yang,et al.  Test strategies for cost-sensitive decision trees , 2006, IEEE Transactions on Knowledge and Data Engineering.

[12]  Charles Ling,et al.  A Comparative Study of Cost-Sensitive Classifiers , 2007 .

[13]  Cheng-Lin Liu,et al.  Handwritten Chinese Character Recognition: Effects of Shape Normalization and Feature Extraction , 2006, SACH.

[14]  Tianwen Zhang,et al.  Off-line recognition of realistic Chinese handwriting using segmentation-free strategy , 2009, Pattern Recognit..