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摘要智能交通系统在现代交通管理中发挥了越来越重要的作用。作为其中关键组成部 分的车牌识别技术是智能交通中的一个重要的研宄课题。车牌识别系统利用计算机 视觉、图像处理和模式识别等多方面的知识来识别图像中的车牌信息，在实际生活 中有非常大的应用前景。22055
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车牌识别主要分为车牌定位、字符切分、字符识别3个主要的阶段。车牌定位作 为车牌识别的最初阶段，主要就是从车牌图像中定位出车牌区域，由于车牌区域边 缘信息较为丰富，采用了改进的Sobel算法用于提取车牌图像的边缘信息，再利用边 缘密度分析的方法就可以准确快速定位出车牌区域。字符切分是在定位出来的车牌 区域切割出字符图像用于字符识别。由于字符排列规整，字符之间的距离也差不多 一样，将车牌区域二值化之后利用连通域分析方法搜索标注车牌区域的连通域，通 过连通域的位置和大小确定字符的位置和大小。
字符识别作为车牌识别的最后一个阶段，其识别结果直接决定了车牌识别系统的 好坏，字符识别算法需要克服车牌定位和字符切分不准确带来的影响，同时需要控 制整个系统的对伪车牌的误检率。对于普通车牌字符，采用多模板匹配的识别算法 能够比较快速准确的识别出字符，对于相似字符和汉字采用基于神经网络的识别算 法能够获得比较理想的识别结果。最后通过将模板匹配算法的互相关算子转换为识 别置信度，统计字符识别的平均置信度，通过置信度来去除伪车牌，获得了比较理 想的效果。
车牌识别系统是一个实时性要求比较高的系统，本文采取的方法兼顾了识别的准 确率和实时性要求。对1000张测试图像的实验结果表明，本文提出的算法能快速准 确定位出车牌区域，准确切割车牌字符图像，识别率较高，运算速度较快，具有较 大的应用前景。
毕业论文关键词：车牌识别多模板匹配神经网络伪车牌
Abstract
Intelligent transportation system (ITS) plays an increasingly important role in modern traffic management. As the crucial component of intelligent transportation system, license plate recognition (LPR) is a very important research issue in intelligent transportation system. License plate recognition system uses the knowledge of computer vision, image processing and pattern recognition to recognition the license plate, it has a very large application prospect.
License plate recognition is pided into three major phases: license plate location, character segmentation, character recognition. License plate location as the first stage of license plate recognition, locate the license plate mainly based on the edge information of plate area. Because there are abundant edges in plate area, we use improved sobel algorithm to extract edge, and then edge density analysis method to locate the plate region accurately and quickly. Character segmentation is to get the character image for character recognition after the plate location. Because the characters are ranged regularly and the distances between each characters are all most the same, first we binary the license plate region, then use the connected component analysis method to search the marked connected component, we can determine the size and position of the characters based on the position and size of the connected components.
Character recognition as the final stage of license plate recognition system, the recognition result directly determines the quality of license plate recognition system, the character recognition algorithm needs to deal with the impact of inaccuracy that locate the plate area and segment character, the algorithm also needs to control the false detection rate of the whole system. For the ordinary character recognition, multi-template matching algorithm is used to recognize character quickly and accurately, neural network recognition algorithm is used to recognize the similar characters and Chinese characters. Finally, we treat the cross-correlation operator of multiple template matching algorithm as the confidence degree of recognition, calculate the average confidence degree of recognition, remove the pseudo license plate based on the average confidence degree of recognition, obtained desired results.
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