基于神经网络和综合特征的车牌定位算法  

Algorithm of Car Plate Location Based on Neural Network and Integrated Features

作　　者：王森[1] 陈炬桦[1] 

机构地区：[1]中山大学,广东广州510275

出　　处：《计算机技术与发展》2008年第2期38-41,共4页Computer Technology and Development

摘　　要：文中提出一种基于神经网络,利用车牌颜色、字符分布特征来提取车牌的算法。与以前的神经网络定位车牌不同的是,本算法是用二值化后每个8-连接对象作为网络的输入。这样可以减少训练样本数目,有针对性地训练噪音。实验证明本算法对于复杂背景的车牌有较好的提取效果,并且有较快的执行速度和较好的鲁棒性。Presents an algorithm which makes use of information such as color of the car plate and the distribution of the car plate characters based on neural network to extract the car plate. The algorithm uses every 8 - connection objects as the input of the neural network which is different from other neural network location of car plate. The method could reduce the sample used for training the network and training the noise of the picture directly. Experiments show that method is effective and robust for the photo which have complicated backgrounds and meantime execute very fast.

关 键 词：车牌定位 神经网络 颜色空间 灰度共生矩阵 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]