一种基于BP神经网络的车牌字符分类识别方法

A New Method of License Plate Characters Classified Recognition Based on BP Neural Networks

目前,车牌字符识别算法主要是基于模板匹配、特征匹配或神经网络的方法。本文根据车牌字符的特殊性,提出一种采用特征提取与BP神经网络学习算法相结合的分类识别技术,选取字符的粗网格特征作为字符的识别特征,以改进后的归一化字符原始特征直接输入到BP神经网络分类器中进行车牌字符识别研究。对于易混淆和相似的字符、汉字笔划粘连、字符偏移现象等都提出了自己的解决方法。实验结果说明,本方法可大幅提高车牌识别系统的正确识别率和抗干扰能力。

In this paper, we choose the feature of rough grid as the feature of character recognition, and directly input the improved unitary character originality feature to BP neural network classifier to recognize the license plate character. To improve the recognition rate of the character recognition system for vehicle license plate, the paper analyzed the result of test, and put forward a series of measure, for example, designed a careful neural network classifier to distill detail feature of character which is analogical and promiscuous. Found on Chinese characters stroke conglutination and character excursion phenomena, based on standard stylebook, for some baroque strokes compactness province characters, representative stroke conglutination stylebook and representative excursion stylebook were suitably added to Chinese character＇s network training stylebook. The empirical result shows that the method can maximally improve the solidity performance of character network.