基于字符特征叠加提取与BP神经网络的字符识别

由于字符的种类繁多，并且同一字符又有多种字体，而传统的字符识别方法不能充分利用字符本身的特征，因此造成识别的字符种类单一、识别效果不理想等问题。提出一种通过字符特征叠加提取结合 BP 神经网络识别字符的方法，从单一字符图像中提取到更多的字符特征，利用BP神经网络自我学习的特点，设计了字符识别系统，再用 VC编程完成识别过程的仿真。结果证明，用本文提出的方法进行字符识别，识别的字符种类多、识别率高、识别时间短。

Research on Character Recognition Based on Repeated Character Feature Extraction and BP Network

There are many types of characters,and each type of character has a variety of fonts.The traditional character recognition methods can not make full use of the features of the character itself.Therefore,only one type of character can be recognized and the recognition effect is not ideal.A kind of character recognition method was presented based on repeated character feature extraction and BP neural network.The proposed method extracted more features of the character itself for a single character image,and the character recognition system by taking advantage of the BP neural network self-learning characteristic was designed.Finally,the recognition process simulation with VC programming was completed.The VC simulation showed that the proposed method could recognize many types of characters,the recognition rate was high and the recognition time was short.