提出了一种新的基于统计与模糊隶属度的光学字符特征提取方法，可以快速准确地识别受噪声污染的光学字符。相比传统算法，本文方法的特征空间区分度更高，最小类间距离扩大33.2%以上。应用在径向基函数(RadicalBasis Function，RBF)神经网络中，在字体字号变化且有背景噪声污染的影响下，识别率高达99%以上，且相比直方图投影法提速75%。理论分析与实验结果表明，与传统方法相比，该算法抗噪能力更强、模式区分度更高、时空复杂度更低，更简约、更全面地覆盖了字符的特征，应用范围广。已应用于实际系统，取得很好的实验结果。

To recognize optical character with noise pollution rapidly and accurately, a novel approach for character feature extraction based on statistics and fuzzy membership is proposed. Compared with traditional method, this approach has a higher degree of differentiation in feature space increasing 33% of minimum inter-class distance. Applied in Radical Basis Function (RBF) neural network, under the influence of different font size and image background noise pollution, character recognition rate is up to 75%. Theoretical analysis and experimental results show that, compared with traditional methods, this approach achieves a better anti-noise performance, greater degree of differentiation and lower time and space complexity. It can be simpler, more comprehensive coverage characters' features with wide application. This approach has been applied to the actual system and achieves good results.