论文编号:JD028   字数:21214,页数:47

摘要：近年来随着国民经济的蓬勃发展，国内高速公路、城市道路、停车场建设越来越多，对交通控制、安全管理的要求也日益提高。因此，汽车牌照识别技术在公共安全及交通管理中具有特别重要的实际应用意义。本文对车牌识别系统中的车牌定位、字符分割和字符识别进行了初步研究。对车牌定位，本文采用投影法对车牌进行定位；在字符分割方面，本文使用阈值规则进行字符分割；针对车牌图像中数字字符识别的问题，本文采用了基于BP神经网络的识别方法。在学习并掌握了数字图像处理和模式识别的一些基本原理后，使用VC++6.0软件利用以上原理针对车牌识别任务进行编程。实现了对车牌的定位和车牌中数字字符的识别。

关键词：车牌定位；字符分割；BP神经网络；车牌识别；VC++

Abstract:

In recent years, with the vigorous development of the national economy，there are more and more construct in the domestic expressway, urban road, and parking area. The requisition on the traffic control, safety management improves day by day. Therefore, license plate recognition technology has the particularly important practical application value in the public security and the traffic control. In the paper, a preliminary research was made on the license location, characters segment and characters recognition of the license plate recognition. On the license location，the projection was used to locate the license plate; On the characters segmentation, the liminal rule was used to divide the characters; In order to solve the problem of the digital characters recognition in the plate, BP nerve network was used to recognize the digital characters. After studying and mastering some basic principles of the digital image processing and pattern recognition, the task of  license plate recognition was programmed with VC++ 6.0 using above principles. The license location and the digital characters recognition in the license plate were implemented.

Keywords: license location,  characters segmentation,  BP nerve network,  license plate recognition,  VC++

目  录
第1章绪论 ......................................................................1
1.1 课题研究背景 ................................................................1
1.2 车辆牌照识别系统原理 ........................................................1
1.3 车辆牌照识别在国内外研究现状 ................................................2
1.4 本文主要工作及内容安排 ......................................................3
第2章车辆牌照的定位方法........................................................ 4
2.1 车辆牌照图像的预处理 ........................................................4
2.1.1 256色位图灰度化........................................................... 4
2.1.2 灰度图像二值化.............................................................5
2.1.3 消除背景干扰去除噪声.......................................................6
2.2 车辆牌照的定位方法简介.......................................................6
2.3 系统采用的定位方法 ..........................................................7
2.3.1 车辆牌照的水平定位 ........................................................7
2.3.2 车辆牌照的垂直定位........................................................ 7
2.3.3 定位的算法实现 ............................................................10
2.4 实验结果分析................................................................ 12
第3章  车辆牌照的字符分割........................................................13
3.1 车牌预处理 ..................................................................13
3.1.1 去边框处理 ................................................................13
3.1.2 去噪声处理................................................................ 13
3.1.3 梯度锐化 ..................................................................15
3.1.4 倾斜调整 ..................................................................16
3.2 字符分割方法简介............................................................ 17
3.3 系统采用的分割方法 ..........................................................19
3.3.1 算法介绍...................................................................19
3.3.2 算法的实现 ................................................................20
3.4 字符分割实验结果............................................................ 21
第4章  特征提取与字符识别 .......................................................22
4.1 字符的特征提取 ..............................................................22
4.2 字符的识别方法简介 ..........................................................23
4.3 系统采用的识别方法 ..........................................................24
4.3.1 人工神经网络简介 ..........................................................24
4.3.2  BP神经网络识别车牌 .......................................................25
4.3.3  BP神经网络识别算法实现 ...................................................28
4.4 实验结果分析 ................................................................29
总结 ............................................................................32
致谢 ............................................................................33
参考文献 ........................................................................34

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