DISCRIMINATIVE TRAINING BASED QUADRATIC CLASSIFIER FOR HANDWRITTEN CHARACTER RECOGNITION

2007 ◽  
Vol 21 (06) ◽  
pp. 1035-1046 ◽  
Author(s):  
RUI ZHANG ◽  
XIAO QING DING ◽  
HAI LONG LIU
Keyword(s):  
Maximum Likelihood ◽  
Character Recognition ◽  
Discriminative Training ◽  
Classification Error ◽  
Chinese Characters ◽  
Handwritten Character Recognition ◽  
Handwritten Character ◽  
Quadratic Discriminant Function ◽  
Minimum Classification Error ◽  
Ml Estimator

In offline handwritten character recognition, the classifier with modified quadratic discriminant function (MQDF) has achieved good performance. The parameters of MQDF classifier are commonly estimated by the maximum likelihood (ML) estimator, which maximizes the within-class likelihood instead of directly minimizing the classification errors. To improve the performance of MQDF classifier, in this paper, the MQDF parameters are revised by discriminative training using a minimum classification error (MCE) criterion. The proposed algorithm is applied to recognizing handwritten numerals and handwritten Chinese characters, the recognition rates obtained are among the highest that have ever been reported.

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