Handwritten Character Recognition Using Combined Gradient and Wavelet Feature

A novel feature based on the combination of gradient feature and coefficients of wavelet transform is developed in this paper. In handwritten character recognition, the gradient feature represents local characteristic properly, but it is sensitive to the deformation of handwritten character. Meanwhile, wavelet transform represents the character image in multiresolution analysis and keeps adequate global characteristic in different scales. In order to improve the discrimination power, we composed local and global characteristic in a combined feature. Three combination schemes are described in this paper. Experiments are conducted on two Chinese character databases, ETL8B subset (197 categories) and HKBU-SC110 (110 categories), to test the performance of proposed feature. The recognition accuracies of our feature achieve 95.53% and 93.77% for ETL8B subset and HKBU-SC110 by 1-NN classifier, respectively, which are higher than those of gradient feature