基于Gabor变换的高鲁棒汉字识别新方法

本文提出了针对字符图像的基于Gabor变换的汉字识别新方法.在对Gabor变换深入分析的基础上,本文针对汉字图像的统计信息,提出了一种有效的Gabor滤波器组参数优化方法;同时,对Gabor滤波器组的输出进行非线性变换,使其适应不同亮度和低质量灰度字符图像的识别.本文还改进了分块特征的抽取算法,提高了对字符细节的分辨能力.实验表明,这种特征抽取方法大大加强了识别系统抵御图像噪声、干扰、亮度变化、笔画模糊、笔画断裂以及字符形变的能力,在应用于各种低质量的二值或者灰度的印刷和脱机手写字符图像识别时,能获得较其他算法更良好的识别性能.

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.