Improving Handwritten Chinese Character Recognition with Discriminative Quadratic Feature Extraction

Discriminative feature extraction (DFE) is an effective linear dimensionality reduction method for pattern recognition. It improves the recognition performance via optimizing subspace projection axes and classifier parameters simultaneously. In this paper, we propose a nonlinear extension of DFE, called discriminative quadratic feature extraction (DQFE), for which feature vectors are firstly mapped to a high-dimensional nonlinear space and then projected to a low-dimensional subspace learned by DFE. The nonlinear mapping is obtained by adding quadratic (correlation or covariance) features computed directly on the original gradient feature maps with different region partition. In this way, both the structural information of the image and the correlation information of features are used to generate a nonlinear high-dimensional feature mapping (thousands of dimensions). Experimental results demonstrated that DQFE can improve the accuracy for different classifiers in handwritten Chinese character recognition.