A comparative study of gabor feature and gradient feature for handwritten chinese character recognition
Gabor feature and gradient feature have been proven to be two most efficient features for handwritten character recognition recently. However, few comprehensive comparative researches on the performance of these two methods in large scale handwritten Chinese character recognition (HCCR) were reported in the literature. In this paper, we compare these two methods for large scale HCCR. Some new interesting conclusions were obtained through this study. The result showed that the performance of gradient feature significantly outperforms gabor feature. Multi-channel Gabor feature can improve the performance of single-channel gabor feature. We also observed that the recognition accuracy hardly get obvious increasing after the number of directions of gabor feature achieves 5. That means only 5 directional parameters of gabor features are necessary to achieve a good enough performance, rather than 8 directions which are widely used in the literature.