基于BP神经网络的手写体数字识别

手写体数字识别是多年来的研究热点，也是字符识别中的一个特别问题。由于手写体数字字体变化很大，传统的识别方法很难达到高的识别率。针对传统的数字识别方法的复杂性和局限性，提出了一种基于BP神经网络的手写体数字的识别方法。该方法在提取手写体数字点特征、笔划密度特征基础上，利用改进的BP神经网络进行训练识别。经实验，识别率达94％。实验结果表明，该方法对手写体数字识别效果良好，不仅简化了传统识别的繁杂性，而且提高了识别的准确性。

Handwritten Numeral Recognition Based on BP Neural Network

Handwritten numeral recognition is a hotspot of study for years,and is an especial issue of character recognition.On account of great changes of handwritten font,it is very difficult for the traditional method of recognition to achieve high recognition rate.To counter the complexity and limitation of traditional digital recognition methods,a kind of handwritten numeral recognition method based on BP neural network is proposed.The point feature and stroke density feature for handwritten digits are extracted;then an improved BP neural network is applied to classify handwritten digits by those features.Via experiment,the recognition rate is 94%.Experiments show that the proposed approach has a good effect on handwritten numeral recognition.It not only simplifies the complexity of the traditional recognition,but also increases the accuracy of recognition.