LDA-Based Compound Distance for Handwritten Chinese Character Recognition

In this paper, we propose a linear discriminant analysis (LDA)-based compound distance measure for discriminating similar characters in handwritten Chinese character recognition. The previous compound Mahalanobis function (CMF) is shown to be a special case of the proposed method. On finding similar character pairs by cross-validation using a baseline classifier, LDA is applied to each similar pair, and the LDA-based distance measure is combined with the discriminant function of the baseline classifier. In our experiments on the ETL9B and CASIA databases using the modified quadratic discriminant function (MQDF) as baseline classifier, the LDA-based compound distance is demonstrated to outperform the previous compound function methods.