Gabor Filters Based Feature Extraction for Robust Chinese Character Recognition

This paper proposed a new feature extraction method for Chinese character recognition by using Gabor filters. Based on the theory of Gabor filters and the statistical information of Chinese character images, an effective method to design Gabor filters was developed. Moreover,to improve the performances for low quality images,non- linear functions were designed to regulate the outputs of Gabor filters adaptively. This paper also improved the feature extraction method to enhance the discriminability of histogram features . Experiments show that our method performs excellently for images with noises, backgrounds or stroke distortions and can be applied to printed or handwritten character recognition tasks in low quality greyscale or binary images.