基于BP网络的车牌字符识别_毕业设计(论文)

本科毕业设计（论文）资料

本科毕业设计（论文）资料第一部分毕业论文

本科毕业设计（论文）

2013年6月

摘要

基于BP网络的车牌字符识别是一门对车牌字符识别的技术，它的产生是为了完善智能交通系统，使得交通系统更具有信息时代意义。

本文利用BP神经网络与图像处理技术相结合的方法，将BP神经网络应用到车牌字符识别中。针对车牌图像的处理的过程包括：车牌图像去噪、车牌图像灰度化、车牌图像二值化、车牌字符图像分割、车牌字符图像归一化、车牌字符图像特征值提取。前面五个过程是为了保证字符信息能更好的体现出来有利于将特征值得提取。BP神经网络通过对组建的车牌字符库的学习后才会具有识别功能，然后将车牌字符图像提取到的特征值送入到BP神经网络中就能识别出来。

通过实验证明了通过上述的过程是能够将车牌字符识别出来，在这个识别过程中对于BP网络训练的收敛性是十分重要的，本文认为可以通过修改隐含层节点的个数、训练函数和激发函数来完成BP网络的训练以使得BP神经网络具有识别功能。对于识别的关键部分在于对特征值的提取，只有采可靠的提取办法才能保证字符信息部丢失这样才有利于识别。

关键词：车牌字符识别，BP神经网络，特征值提取

ABSTRACT

BP network based license plate character recognition is one pair of license plate character recognition technology, which is produced in order to improve intelligent transportation system, making the transport system more meaningful information age.

In this paper, BP neural network and image processing technology, a combination of methods will be applied to the license plate BP neural network character recognition. For the license plate image processing process includes: license plate image denoising, gray plate image, license plate image binarization, license plate character segmentation, license plate character image normalization, license plate character image feature extraction. During the previous five character information in order to ensure better reflected the benefit is worth the feature extraction. Through the formation of BP neural network library for license plate character recognition function after learning will have, and then extract the license plate character image characteristic value fed to BP neural network can be identified.

The experimental results show the process by the above license plate characters can be identified, in this process for identifying convergence BP network training is very important that this can modify the number of nodes in the hidden layer, training function and stimulate function to complete BP network training to enable BP neural network has recognition. For the identification of the key part of the feature value extraction, mining only reliable way to ensure the character information extracting unit lost that help identify, extract the paper also proposed several ways.

Keywords: LPR，BP neural network，Feature extraction

目录

摘要............................................................. I ABSTRACT .......................................................... I I 目录........................................................... I II 第1章绪论. (1)

1.1 车牌识别技术 (1)

1.1.1 车牌识别技术 (1)

1.1.2 智能交通系统 (2)

1.2国内外研究现状 (4)

1.3本文研究内容 (4)

第2章字符识别方法 (6)

2.1 车牌图像预处理 (6)

2.1.1 车牌规律 (6)

2.1.2车牌图像去噪 (7)

2.1.3车牌图像的灰度化和二值化 (7)

2.2 字符分割 (9)

2.2.1 边缘检测 (10)

2.2.2 字符切割 (11)

2.2.3 字符图像归一化 (12)

2.2.4字符特征值提取 (12)

2.3 BP神经网络 (14)

2.3.1 BP网络 (15)

2.3.2 BP网络的模型结构 (15)

2.3.3 BP网络算法 (17)

第3章基于BP网络的字符识别 (20)

3.1 车牌图像预处理实现 (20)

3.1.1 车牌图像滤波实现 (20)

3.1.1 灰度化技术及二值化实现 (20)

3.1.2 车牌图像分割实现 (23)