摘要
以定位、分割后的机动车号牌字符为研究对象.首先,对机动车号牌图像进行大小、灰度方差、灰度均值的标准化处理.其次,根据机动车号牌字符的特点,抽取字符3种不同的特征,构造3个BP神经网络对机动车号牌字符进行识别.并根据字符在机动车号牌中所处位置的差异,在每个BP神经网络中构造4种不同的子网络分别进行训练和识别.最后,每个BP神经网络的输出通过加权求和的组合方式得到最终识别结果.在组合各网络输出前,采用对字符图像求取局部二阶差分值的方法,将字形相近的字符进行再分类,从而有效地降低误识率.通过分析实验结果,表明本算法在机动车号牌识别应用中达到了理想的识别效果.
Aiming at located and partitioned characters on motor vehicle plates. First, we standardized the size, variance of gray scale and mean value of gray scale of the motor vehicle plate images. Second, according to the characteristic of characters on the plate, we selected three different characteristics, and constructed three BP nerve nets (NN) to identify the characters on the motor vehicle plate. At the same time, for the position differences of characters on the plate we constructed and trained four different subnets to recognize characters in every BP NN. Finally, we used weighted summation of out-up of every BP NN to get the final result. Furthermore, before combining the result of each BP NN, we calculated part two-rank difference in the (characters') image to secondly classify the similar characters in the shape, and so that we can decrease the (wrong) identification rate. According to the analysis based on the results of the experiments, it is demonstrated that the algorithm has a perfect identification effect.
出处
《吉林大学学报（理学版）》
CAS
CSCD
北大核心
2005年第4期461-466,共6页
Journal of Jilin University:Science Edition
基金
国家863项目基金(批准号:2003AA1Z2141).
关键词
模式识别
字符识别
神经网络
BP算法
<Keyword>pattern recognition
character recognition
neural network
BP algorithm
