Similar Pattern Discriminant Analysis for Improving Chinese Character Recognition Accuracy

In this paper, a similar pattern discriminant analysis method is proposed. It optimizes the feature projection matrix based on similar pattern pairs and aims to extract targeted features for similar pattern discrimination. For improving Chinese character recognition accuracy, we introduce a cascade modified quadratic discriminant function (MQDF) model to combine linear discriminant analysis (LDA) and similar pattern discriminant analysis. The proposed method is investigated and compared with compound Mahalanobis function (CMF) on two data sets. The results indicate that the cascade MQDF achieves a better improvement and higher recognition accuracies than CMF. The relative recognition errors have been decreased up to 19.73% and 15.59% respectively on HCL2000 and THU-HCD datasets with respect to single MQDF.

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