汽车车牌识别系统-车牌定位子系统的设计与实现大学毕业论文外文文献翻译及原文
汽车车牌识别系统-车牌定位子系统的设计与实现大学毕业论文外文文献翻译及原文
毕业设计（论文）外文文献翻译文献、资料中文题目：汽车车牌识别系统-车牌定位子系统的设计与实现文献、资料英文题目：文献、资料来源：文献、资料发表（出版）日期：院（部）：专业：班级：姓名：学号：指导教师：翻译日期： 2017.02.14汽车车牌识别系统---车牌定位子系统的设计与实现摘要汽车车牌识别系统是近几年发展起来的计算机视觉和模式识别技术在智能交通领域应用的重要研究课题之一。
在车牌自动识别系统中，首先要将车牌从所获取的图像中分割出来实现车牌定位，这是进行车牌字符识别的重要步骤，定位的准确与否直接影响车牌识别率。
本次毕业设计首先对车牌识别系统的现状和已有的技术进行了深入的研究，在此基础上设计并开发了一个基于MATLAB的车牌定位系统，通过编写MATLAB文件，对各种车辆图像处理方法进行分析、比较,最终确定了车牌预处理、车牌粗定位和精定位的方法。
本次设计采取的是基于微分的边缘检测，先从经过边缘提取后的车辆图像中提取车牌特征，进行分析处理，从而初步定出车牌的区域，再利用车牌的先验知识和分布特征对车牌区域二值化图像进行处理，从而得到车牌的精确区域，并且取得了较好的定位结果。
关键词：图像采集，图像预处理，边缘检测，二值化，车牌定位ENGLISH SUBJECTABSTRACTThe subject of the automatic recognition of license plate is one of the most significant subjects that are improved from the connection of computer vision and pattern recognition. In LPSR, the first step is for locating the license plate in the captured image which is very important for character recognition. The recognition correction rate of license plate is governed by accurate degree of license plate location.Firstly, the paper gives a deep research on the status and technique of the plate license recognition system. On the basis of research, a solution of plate license recognition system is proposed through the software MATLAB,by the M-files several of methods in image manipulation are compared and analyzed. The methods based on edge map and das differential analysis is used in the process of the localization of the license plate, extracting the characteristics of the license plate in the car images after being checked up for the edge, and then analyzing and processing until the probably area of license plate is extracted，then come out the resolutions for localization of the car plate.KEY WORDS：imageacquisition，image preprocessing，edge detection，binarization，licence，license plate location前言 (1)第1章绪论 (2)§1.1 课题研究的背景 (2)§1.2 车牌的特征 (2)§1.3 国内外车辆牌照识别技术现状 (3)§1.4车牌识别技术的应用情况 (4)§1.5 车牌识别技术的发展趋势 (5)§1.6车牌定位的意义 (6)第2章 MATLAB简介 (7)§2.1 MATLAB发展历史 (7)§2.2 MATLAB的语言特点 (7)第3章图像预处理 (10)§3.1 灰度变换 (10)§3.2 图像增强 (11)§3. 3 图像边缘提取及二值化 (13)§3. 4 形态学滤波 (18)第4章车牌定位 (21)§4.1车牌定位的主要方法 (21)§4.1.1基于直线检测的方法 (22)§4.1.2 基于阈值化的方法 (22)§4.1.3 基于灰度边缘检测方法 (22)§4.1.4 基于彩色图像的车牌定位方法 (25)§4.2 车牌提取 (26)结论 (30)参考文献 (31)致谢 (33)随着交通问题的日益严重，智能交通系统应运而生。
车牌定位-本科毕业设计论文
交通图象检测与处理方法研究对于交通安全、交通管理与控制具有非常重要的理论意义和实用价值。
通过视频图象的检测与识别,可以实时检测交通违章现象、识别违章车辆的车牌号码，为公安交通管理部门提供强有力的执法证据。
因此，研究交通图象检测与处理方法对智能交通运输系统的发展具有重要的推动作用。
本系统着力对车牌的识别过程进行研究和实现。
主要能够对带有车牌的图片灰度化，二值化，中值滤波等处理，并能够截取车牌图片。
车牌定位是指将车牌区域从车辆图像中分割出来，是实现整个系统的关键环节。
而车牌定位主要包含两个关键技术问题：图像的预处理和车牌定位的算法。
本论文主要应用VC语言编程，对其车牌图像进行预处理，有效的解决一些导致识别、定位错误的问题。
关键词：车牌定位,二值化,预处理Traffic image processing method for testing and research, traffic safety management and control has important theoretical significance and practical value. Through video images of detection and recognition can real-time detection and identification of violate the traffic violations phenomenon plate number for public security traffic management department, provide strong evidence of law enforcement.The focus on the license plate identification system research and implementation process. Mainly with the license plate on the picture to gray level transformation, binarization, median filtering and other processing, and can intercept license plate image.License plate location is license plate recognition technology a vital part . License plate location refers to the license plate out from the vehicle image segmentation is the key to the entire system. The license plate location primarily consists of two key technologies: image preprocessing and license plate location algorithm. Main application VC language program, to the license plate identification, orientation, image analysis, processing. And some of the mistakes in recognition, positioning problem.Keywords:Plate Positioning,Binarization ,Pretreatment目录1 前言 (1)2 车牌定位系统概述 (2)2.1 车牌定位系统基础 (2)2.1.1 我国车辆与车牌现状 (2)2.1.2 车牌定位的研究意义 (2)2.1.3 国内外学者研究现状 (3)2.2 图像处理技术基础 (4)2.2.1 数字图像基本知识 (4)2.2.2 数字图像预处理 (4)2.2.3 数字图像问题剖析 (6)2.2.4 开发相关知识 (6)3 车牌定位于提取技术 (7)3.1 车牌定位与提取流程 (7)3.2 预处理过程 (8)3.2.1 图像的灰度化处理 (8)3.2.2 直方图均衡化 (9)3.2.3 图像的二值化 (11)3.2.4 中值滤波 (14)3.3 车牌区域定位与分割 (17)3.3.1 车牌特征 (17)3.3.2 车牌分割 (18)3.3.3 彩色分割 (20)3.3.4 基于投影的精确定位 (23)4 总结 (29)4.1 论文总结 (29)4.2 问题改进与展望 (30)4.3 心得体会 (31)致谢 (32)参考文献 (33)1 前言随着国民经济的飞速发展，交通状况日益恶化，这几乎成为所有大中城市的通病。
车牌识别外文文献翻译中英文
外文文献翻译(含：英文原文及中文译文)文献出处：Gao Q, Wang X, Xie G. License Plate Recognition Based On Prior Knowledge[C]// IEEE International Conference on Automation and Logistics. IEEE, 2007:2964-2968.英文原文License Plate Recognition Based On Prior KnowledgeQian Gao, Xinnian Wang and Gongfu XieAbstract - In this paper, a new algorithm based on improved BP (back propagation) neural network for Chinese vehicle license plate recognition (LPR) is described. The proposed approach provides a solution for the vehicle license plates (VLP) which were degraded severely. What it remarkably differs from the traditional methods is the application of prior knowledge of license plate to the procedure of location, segmentation and recognition. Color collocation is used to locate the license plate in the image. Dimensions of each character are constant, which is used to segment the character of VLPs. The Layout of the Chinese VLP is an important feature, which is used to construct a classifier for recognizing. The experimental results show that the improved algorithm is effective under the condition that the license plates were degraded severely.Index Terms - License plate recognition, prior knowledge, vehiclelicense plates, neural network.I. INTRODUCTIONV ehicle License-Plate (VLP) recognition is a very interesting but difficult problem. It is important in a number of applications such as weight-and-speed-limit, red traffic infringement, road surveys and park security [1]. VLP recognition system consists of the plate location, the characters segmentation, and the characters recognition. These tasks become more sophisticated when dealing with plate images taken in various inclined angles or under various lighting, weather condition and cleanliness of the plate. Because this problem is usually used in real-time systems, it requires not only accuracy but also fast processing. Most existing VLP recognition methods [2], [3], [4], [5] reduce the complexity and increase the recognition rate by using some specific features of local VLPs and establishing some constrains on the position, distance from the camera to vehicles, and the inclined angles. In addition, neural network was used to increase the recognition rate [6], [7] but the traditional recognition methods seldom consider the prior knowledge of the local VLPs. In this paper, we proposed a new improved learning method of BP algorithm based on specific features of Chinese VLPs. The proposed algorithm overcomes the low speed convergence of BP neural network [8] and remarkable increases the recognition rate especially under the condition that the license plate images were degrade severely.II. SPECIFIC FEA TURES OF CHINESE VLPSA. DimensionsAccording to the guideline for vehicle inspection [9], all license plates must be rectangular and have the dimensions and have all 7 characters written in a single line. Under practical environments, the distance from the camera to vehicles and the inclined angles are constant, so all characters of the license plate have a fixed width, and the distance between the medium axes of two adjoining characters is fixed and the ratio between width and height is nearly constant. Those features can be used to locate the plate and segment the individual character. B. Color collocation of the plateThere are four kinds of color collocation for the Chinese vehicle license plate .These color collocations are shown in table I.TABLE IMoreover, military vehicle and police wagon plates contain a red character which belongs to a specific character set. This feature can be used to improve the recognition rate.C. Layout of the Chinese VLPSThe criterion of the vehicle license plate defines the characters layout of Chinese license plate. All standard license plates contain Chinese characters, numbers and letters which are shown in Fig.1. The first one is a Chinese character which is an abbreviation of Chineseprovinces. The second one is a letter ranging from A to Z except the letter I. The third and fourth ones are letters or numbers. The fifth to seventh ones are numbers ranging from 0 to 9 only. However the first or the seventh ones may be red characters in special plates (as shown in Fig.1). After segmentation process the individual character is extracted. Taking advantage of the layout and color collocation prior knowledge, the individual character will enter one of the classes: abbreviations of Chinese provinces set, letters set, letters or numbers set, number set, special characters set.(a)Typical layout(b) Special characterFig.1 The layout of the Chinese license plateIII. THE PROPOSED ALGORITHMThis algorithm consists of four modules: VLP location, character segmentation, character classification and character recognition. The main steps of the flowchart of LPR system are shown in Fig. 2.Firstly the license plate is located in an input image and characters are segmented. Then every individual character image enters the classifier to decide which class it belongs to, and finally the BP network decides which character the character image represents.A. Preprocessing the license plate1) VLP LocationThis process sufficiently utilizes the color feature such as color collocation, color centers and distribution in the plate region, which are described in section II. These color features can be used to eliminate the disturbance o f the fake plate ’ s regions. The flowchart of the plate location is shown in Fig. 3.Fig.3 The flowchart of the plate location algorithmThe regions which structure and texture similar to the vehicle plate are extracted. The process is described as followed:Here, the Gaussian variance is set to be less than W/3 (W is the character stroke width), so 1P gets its maximum value M at the center of the stroke. After convolution, binarization is performed according to a threshold which equals T * M (T<0.5). Median filter is used to preserve the edge gradient and eliminate isolated noise of the binary image. An N * N rectangle median filter is set, and N represents the odd integer mostly close to W.Morphology closing operation can be used to extract the candidate region. The confidence degree of candidate region for being a license plate is verified according to the aspect ratio and areas. Here, the aspect ratio is set between 1.5 and 4 for the reason of inclination. The prior knowledge of color collocation is used to locate plate region exactly. The locating process of the license plate is shown in Fig. 4.2) Character segmentationThis part presents an algorithm for character segmentation based on prior knowledge, using character width, fixed number of characters, the ratio of height to width of a character, and so on. The flowchart of the character segmentation is shown in Fig. 5.Firstly, preprocess the license the plate image, such as uneven illumination correction, contrast enhancement, incline correction and edge enhancement operations; secondly, eliminating space mark which appears between the second character and the third character; thirdly, merging the segmented fragments of the characters. In China, all standard license plates contain only 7 characters (see Fig. 1). If the number of segmented characters is larger than seven, the merging process must be performed. Table II shows the merging process. Finally, extracting the individual character’ image based on the number and the width of the character. Fig. 6 shows the segmentation results. (a) The incline and broken plate image, (b) the incline and distort plate image, (c)the serious fade plate image, (d) the smut license plate image.where Nf is the number of character segments, MaxF is the number of the license plate, and i is the index of each character segment.The medium point of each segmented character is determined by:(3)where 1i Sis the initial coordinates for the character segment, and 2i S is thefinal coordinate for the character segment. The d istance between two consecutive medium points is calculated by:(4)Fig.6 The segmentation resultsB. Using specific prior knowledge for recognitionThe layout of the Chinese VLP is an important feature (as described in the section II), which can be used to construct a classifier for recognizing. The recognizing procedure adopted conjugate gradient descent fast learning method, which is an improved learning method of BP neural network[10]. Conjugate gradient descent, which employs a series of line searches in weight or parameter space. One picks the first descent direction and moves along that direction until the minimum in error is reached. The second descent direction is then computed: this direction the “ conjugate direction” is the one along which the gr adient does not change its direction will not “ spoil ” the contribution from the previous descent iterations. This algorithm adopted topology 625-35-N as shown in Fig. 7. The size of input value is 625 (25*25 ) and initial weights are with random values, desired output values have the same feature with the input values.As Fig. 7 shows, there is a three-layer network which contains working signal feed forward operation and reverse propagation of error processes. The target parameter is t and the length of network outputvectors is n. Sigmoid is the nonlinear transfer function, weights are initialized with random values, and changed in a direction that will reduce the errors.The algorithm was trained with 1000 images of different background and illumination most of which were degrade severely. After preprocessing process, the individual characters are stored. All characters used for training and testing have the same size (25*25 ).The integrated process for license plate recognition consists of the following steps:1) Feature extractingThe feature vectors from separated character images have direct effects on the recognition rate. Many methods can be used to extract feature of the image samples, e.g. statistics of data at vertical direction, edge and shape, framework and all pixels values. Based on extensive experiments, all pixels values method is used to construct feature vectors. Each character was reshaped into a column of 625 rows’ feature vector. These feature vectors are divided into two categories which can be used for training process and testing process.2) Training modelThe layout of the Chinese VLP is an important feature, which can be used to construct a classifier for training, so five categories are divided. The training process of numbers is shown in Fig. 8.As Fig. 8 shows, firstly the classifier decides the class of the inputfeature vector, and then the feature vector enters the neural network correspondingly. After the training process the optimum parameters of the net are stored for recognition. The training and testing process is summarized in Fig. 9.(a) Training process(b)Testing processFig.9 The recognition process3) Recognizing modelAfter training process there are five nets which were completely trained and the optimum parameters were stored. The untrained feature vectors are used to test the net, the performance of the recognition system is shown in Table III. The license plate recognition system is characterized by the recognition rate which is defined by equation (5).Recognition rate =(number of correctly read characters)/ (number of found characters) (5)IV. COMPARISON OF THE RECOGNITION RA TE WITH OTHER METHODSIn order to evaluate the proposed algorithm, two groups of experiments were conducted. One group is to compare the proposed method with the BP based recognition method [11]. The result is shown in table IV. The other group is to compare the proposed method with themethod based on SVM [12].The result is shown in table V. The same training and test data set are used. The comparison results show that the proposed method performs better than the BP neural network and SVM counterpart.V. CONCLUSIONIn this paper, we adopt a new improved learning method of BP algorithm based on specific features of Chinese VLPs. Color collocation and dimension are used in the preprocessing procedure, which makes location and segmentation more accurate. The Layout of the Chinese VLP is an important feature, which is used to construct a classifier for recognizing and makes the system performs well on scratch and inclined plate images. Experimental results show that the proposed method reduces the error rate and consumes less time. However, it still has a few errors when dealing with specially bad quality plates and characters similar to others. This often takes place among these characters (especially letter and number): 3—8 4—A 8—B D—0.In order to improve the incorrect recognizing problem we try to add template-based model [13] at the end of the neural network.中文译文基于先验知识的车牌识别Qian Gao, Xinnian Wang and Gongfu Xie摘要- 本文介绍了一种基于改进的BP（反向传播）神经网络的中国车牌识别（LPR）算法。
车牌照识别系统设计与实现毕业设计论文
车牌照识别系统设计与实现Design and Implementation of Car License Plate Recognition System毕业论文（设计）原创性声明本人所呈交的毕业论文（设计）是我在导师的指导下进行的研究工作及取得的研究成果。
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作者签名：指导教师签名：日期：日期：注意事项1.设计（论文）的内容包括：1）封面（按教务处制定的标准封面格式制作）2）原创性声明3）中文摘要（300字左右）、关键词4）外文摘要、关键词5）目次页（附件不统一编入）6）论文主体部分：引言（或绪论）、正文、结论7）参考文献8）致谢9）附录（对论文支持必要时）2.论文字数要求：理工类设计（论文）正文字数不少于1万字（不包括图纸、程序清单等），文科类论文正文字数不少于1.2万字。
3.附件包括：任务书、开题报告、外文译文、译文原文（复印件）。
4.文字、图表要求：1）文字通顺，语言流畅，书写字迹工整，打印字体及大小符合要求，无错别字，不准请他人代写2）工程设计类题目的图纸，要求部分用尺规绘制，部分用计算机绘制，所有图纸应符合国家技术标准规范。
图表整洁，布局合理，文字注释必须使用工程字书写，不准用徒手画3）毕业论文须用A4单面打印，论文50页以上的双面打印4）图表应绘制于无格子的页面上5）软件工程类课题应有程序清单，并提供电子文档5.装订顺序1）设计（论文）2）附件：按照任务书、开题报告、外文译文、译文原文（复印件）次序装订3）其它摘要汽车牌照自动识别系统是智能交通系统的重要组成部分，是高科技的公路交通监控管理系统的主要功能模块之一，汽车牌照识别技术的研究有重要的现实应用意义。
车辆牌照自动识别的设计与实现
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汽车牌照自动识别系统中英文对照外文翻译文献
汽车牌照自动识别系统中英文对照外文翻译文献(文档含英文原文和中文翻译)Automatic vehicle license plate recognition systemImage processing is not a one step process．We are able to distinguish between several steps which must be performed one after the other until we can extract the data of interest from the observed scene．In this way a hierarchical processing scheme is built up as sketched in Fig．The figure gives an overview of the different phases of image processing.Image processing begins with the capture of an image with a suitable，not necessarily optical，acquisition system．In a technical or scientific application，we may choose to select an appropriate imaging system．Furthermore，we can set up the illumination system，choose the best wavelength range，and select other options to capture the object feature of interest in the best way in an image．Once the image is sensed，it must be brought into a form that can be treated with digital computers.This process is called digitization.With the problems of traffic are more and more serious. Thus Intelligent Transport System (ITS) comes out. The subject of the automatic recognition of license plate is one of the most significant subjects that are improved from the connection of computer vision and pattern recognition. The image imputed to the computer is disposed and analyzed in order to localization the position and recognition the characters on the license plate express these characters in text string form The license plate recognition system (LPSR) has important application in ITS. In LPSR, the first step is for locating the license plate in the captured image which is very important for character recognition. The recognition correction rate of license plate is governed by accurate degree of license plate location. In this paper, several of methods in image manipulation are compared and analyzed, then come out the resolutions for localization of the car plate. The experiences show that the good result has been got with thesemethods. The methods based on edge map and frequency analysis is used in the process of the localization of the license plate, that is to say, extracting the characteristics of the license plate in the car images after being checked up for the edge, and then analyzing and processing until the probably area of license plate is extracted.The automated license plate location is a part of the image processing ,it’s also an important part in the intelligent traffic system．It is the key step in the Vehicle License Plate Recognition(LPR).A method for the recognition of images of different backgrounds and different illuminations is proposed in the paper.the upper and lower borders are determined through the gray variation regulation of the character distribution.The left and right borders are determined through the black-white variation of the pixels in every row.The first steps of digital processing may include a number of different operations and are known as image processing．If the sensor has nonlinear characteristics, these need to be corrected．Likewise，brightness and contrast of the image may require improvement．Commonly，too，coordinate transformations are needed to restore geometrical distortions introduced during image formation．Radiometric and geometric corrections are elementary pixel processing operations．It may be necessary to correct known disturbances in the image，for instance caused by a defocused optics，motion blur，errors in the sensor，or errors in the transmission of image signals．We also deal with reconstruction techniques which are required with many indirect imaging techniques such as tomography that deliver no direct image.A whole chain of processing steps is necessary to analyze and identify objects．First，adequate filtering procedures must be applied in order to distinguish the objects of interest from other objects and the background．Essentially，from an image（or several images），one or more feature images are extracted．The basic tools for this task are averaging and edgedetection and the analysis of simple neighborhoods and complex patterns known as texture in image processing．An important feature of an object is also its motion．Techniques to detect and determine motion are necessary．Then the object has to be separated from the background．This means that regions of constant features and discontinuities must be identified．This process leads to a label image．Now that we know the exact geometrical shape of the object，we can extract further information such as the mean gray value，the area，perimeter，and other parameters for the form of the object[3]．These parameters can be used to classify objects．This is an important step in many applications of image processing，as the following examples show：In a satellite image showing an agricultural area，we would like to distinguish fields with different fruits and obtain parameters to estimate their ripeness or to detect damage by parasites．There are many medical applications where the essential problem is to detect pathologi-al changes．A classic example is the analysis of aberrations in chromosomes．Character recognition in printed and handwritten text is another example which has been studied since image processing began and still poses significant difficulties．You hopefully do more，namely try to understand the meaning of what you are reading．This is also the final step of image processing，where one aims to understand the observed scene．We perform this task more or less unconsciously whenever we use our visual system．We recognize people，we can easily distinguish between the image of a scientific lab and that of a living room，and we watch the traffic to cross a street safely．We all do this without knowing how the visual system works．For some times now，image processing and computer-graphics have been treated as two different areas．Knowledge in both areas has increased considerably and more complex problems can now be treated．Computer graphics is striving to achieve photorealistic computer-generated images of three-dimensional scenes，while image processing is trying to reconstruct one from an image actually taken with a camera．In thissense，image processing performs the inverse procedure to that of computer graphics．We start with knowledge of the shape and features of an object—at the bottom of Fig. and work upwards until we get a two-dimensional image．To handle image processing or computer graphics，we basically have to work from the same knowledge．We need to know the interaction between illumination and objects，how a three-dimensional scene is projected onto an image plane，etc．There are still quite a few differences between an image processing and a graphics workstation．But we can envisage that，when the similarities and interrelations between computergraphics and image processing are better understood and the proper hardware is developed，we will see some kind of general-purpose workstation in the future which can handle computer graphics as well as image processing tasks[5]．The advent of multimedia，i. e. ，the integration of text，images，sound，and movies，will further accelerate the unification of computer graphics and image processing.In January 1980 Scientific American published a remarkable image called Plume2，the second of eight volcanic eruptions detected on the Jovian moon by the spacecraft Voyager 1 on 5 March 1979．The picture was a landmark image in interplanetary exploration—the first time an erupting volcano had been seen in space．It was also a triumph for image processing．Satellite imagery and images from interplanetary explorers have until fairly recently been the major users of image processing techniques，where a computer image is numerically manipulated to produce some desired effect-such as making a particular aspect or feature in the image more visible.Image processing has its roots in photo reconnaissance in the Second World War where processing operations were optical and interpretation operations were performed by humans who undertook such tasks as quantifying the effect of bombing raids．With the advent of satellite imagery in the late 1960s，much computer-based work began and the color composite satellite images，sometimesstartlingly beautiful, have become part of our visual culture and the perception of our planet．Like computer graphics，it was until recently confined to research laboratories which could afford the expensive image processing computers that could cope with the substantial processing overheads required to process large numbers of high-resolution images．With the advent of cheap powerful computers and image collection devices like digital cameras and scanners，we have seen a migration of image processing techniques into the public domain．Classical image processing techniques are routinely employed by graphic designers to manipulate photographic and generated imagery，either to correct defects，change color and so on or creatively to transform the entire look of an image by subjecting it to some operation such as edge enhancement.A recent mainstream application of image processing is the compression of images—either for transmission across the Internet or the compression of moving video images in video telephony and video conferencing．Video telephony is one of the current crossover areas that employ both computer graphics and classical image processing techniques to try to achieve very high compression rates．All this is part of an inexorable trend towards the digital representation of images．Indeed that most powerful image form of the twentieth century—the TV image—is also about to be taken into the digital domain．Image processing is characterized by a large number of algorithms that are specific solutions to specific problems．Some are mathematical or context-independent operations that are applied to each and every pixel．For example，we can use Fourier transforms to perform image filtering operations．Others are“algorithmic”—we may use a complicated recursive strategy to find those pixels that constitute the edges in an image．Image processing operations often form part of a computer vision system．The input image may be filtered to highlight or reveal edges prior to ashape detection usually known as low-level operations．In computer graphics filtering operations are used extensively to avoid abasing or sampling artifacts．翻译：汽车牌照自动识别系统图像处理不是一步就能完成的过程。
车牌识别系统的设计文献综述
车牌识别系统的设计文献综述（机电科学与工程系电气工程及其自动化）摘要：车牌的自动识别是计算机视觉、图像处理与模式识别技术在智能交通领域应用的重要研究课题之一, 是实现交通管理智能化的重要环节, 主要包括车牌识别、字符预处理和特征提取三个关键环节。
车牌识别包括车牌定位、灰度（或彩色）图像二值化、字符切分及字符识别等。
图像预处理包括图像灰度变换、图像增强、图像二值化、梯度锐化、噪声去除、倾斜度调整、车牌边框去除、字符分割、尺寸标准归一化、紧缩重排。
特征提取本文采用逐象素特征提取法来对支付进行识别。
理论上，本系统可以对中国大陆普通汽车车牌的字符进行识别。
关键词：车牌识别，图像预处理，特征提取前言近几年来，随着汽车数量猛增，交通运输系统越来越智能化，这时候行驶车辆的车牌实时识别尤其是交通运输研究的重要组成部分。
车牌识别系统是对公路上配置的摄像头拍摄的照片进行数字图像处理与分析，综合应用于大量的图像处理与分析，利用多种手段以提取车牌区域，进而达到对汽车牌照的精确定位并最终完成对汽车牌照的识别。
识别系统的用途很多，如高速公路电子收费站、公路流量控制、公路稽查、失窃车辆查询等需要车牌认证的场合都要应用车牌识别系统，尤其是高速公路收费系统中，实现不停收费技术可提高公路系统的运行效率，由此可见车牌识别系统具有不可替代的作用，因此对车牌识别技术的研究和应用系统的开发具有重要的现实意义。
正文2.1 模式识别概述模式识别在数字图像处理和分析中占有非常重要的地位，识别所得到的结果往往接近于甚至就是整个数字图像处理和分析的最终结果。
模式识别属于图像分析的范畴，它所得到的结果是一副由明确意义的数值或符号构成的图像或图形文件，而不再是一副具有随机分布性质的图像。
模式识别的目的是对图像中的物体进行分类,或者可以说找出图像中有哪些物体。
分类的依据是从原始图像中提取的不同物体的特征，包括光谱特征、空间纹理特征和时间特征等。
因此，模式识别包括特征提取和分类两方面的内容。
本科毕业论文车牌识别管理系统
摘要随着我国道路的迅猛发展，智能交通系统越来越成为现代交通道路管理的强烈需求。
而类区域性的车辆管理更是成为了需求的热点。
不论是小区还是高校，又或则是高速公路的收费站对于车辆管理的智能化都是有着迫切的期望。
本论文研究的主要内容是将高校作为类区域的典型，从高校的安保以及便捷管理出发，设计了一个基于图像识别的车辆管理系统网站。
从网站的功能划分，到网站的重点功能图像识别出发规划出了网站的雏形。
另外为了网站整体的实现，对网站的重点功能车牌识别中的车牌定位编写了一个专门的java程序对车牌识别进行了分析以及实现。
本文所探究的车牌识别，是基于图像识别的大体处理步骤的包括了车牌的定位、分隔、识别。
其中主要是研究车牌的定位，即从图像的灰度、强化边缘最后再到车牌定位。
其中车牌定位后的分割，以及识别，还有与数据库的比对本文并没有涉及。
程序实现结果表明，车牌定位成功效果比较理想，但是还有一些车牌难以定位。
期待根据这个设计做出的智能车辆管理系统。
关键词车辆管理系统图像识别高校安保目录1 前言 (1)1.1 设计背景与意义 (1)1.2 设计目标 (1)2系统开发环境 (2)2.1 系统配置 (2)2.2 图像识别技术简介 (3)2.3 车牌识别技术简介 (3)3 总体设计 (4)4 详细设计 (5)4.1系统功能模块设计 (5)4.2 图像识别功能设计以及实现 (6)4.2.1 灰度化 (6)4.2.2 灰度直方图 (8)4.2.3 图像均衡化 (9)4.2.4 边缘化 (11)4.2.5 找车牌 (13)4.2.6 二值化 (21)4.3 数据库设计 (24)4.4.1 数据库E-R图设计 (24)4.4.2 创建主要数据库 (26)5 运用读取jar包实现车牌号码识别 (27)6 总结与展望 (34)6.1 总结 (34)6.2 展望 (34)致谢 (36)1 前言1.1 设计背景与意义汽车工业产生一百多年来，一直都被当成是工业发达国家的经济指标，在国家的实际成长中发挥着非常重要的作用。
汽车牌照识别系统的车牌定位技术研究外文资料翻译(适用于毕业论文外文翻译+中英文对照)
建立一个自动车辆车牌识别系统车辆由于数量庞大的抽象,现代化的城市要建立有效的交通自动系统管理和调度.最有用的系统之一是车辆车牌识别系统,它能自动捕获车辆图像和阅读这些板块的号码在本文中，我们提出一个自动心室晚电位识别系统，ISeeCarRecognizer,阅读越南样颗粒在交通费的注册号码.我们的系统包括三个主要模块：心室晚电位检测，板数分割和车牌号码识别。
在心室晚电位检测模块，我们提出一个有效的边界线为基础Hough变换相结合的方法和轮廓算法.该方法优化速度和准确性处理图像取自不同职位。
然后,我们使用水平和垂直投影的车牌号码分开心室晚电位分段模块.最后，每个车牌号码将被OCR的识别模块实现了由隐马尔可夫模型。
该系统在两个形象评价实证套并证明其有效性是适用于实际交通收费系统。
该系统也可适用于轻微改变一些其他类型的病毒样颗粒。
一.导言车牌识别的问题是一个非常有趣，且困难的一个问题.这在许多交通管理系统中是非常有用的。
心室晚电位识别需要一些复杂的任务,如车牌的检测，分割和识别。
这些任务变得更加复杂时，处理各种倾斜角度拍摄的图像或含有噪音的图像。
由于此问题通常是在实时系统中使用，它不仅需要准确性,而且要效率.大多数心室晚电位识别应用通过建立减少一些复杂的约束的位置和距离相机车辆，倾斜角度。
通过这种方式，车牌识别系统的识别率已得到明显改善.在此外，我们可以更准确地获得通过一些具体的当地样颗粒的功能，如字符数，行数在一板,或板的背景颜色或的宽度比为一板高。
二.相关工作心室晚电位的自动识别问题在20世纪90年代开始就有研究。
第一种方法是基于特征的边界线。
首次输入图像处理，以丰富的边界线的一些信息如梯度算法过滤器,导致在一边缘图像.这张照片是二值化处理，然后用某些算法，如Hough 变换，检测线。
最终,2平行线视为板候选人［4] ［5］。
另一种方法是基于形态学[2］。
这种方法侧重于一些板块图像性质如亮度，对称，角度等。
车牌识别系统的设计与实现毕业设计论文
本科生毕业设计（论文）题目：车牌识别系统的设计与实现毕业设计（论文）原创性声明和使用授权说明原创性声明本人郑重承诺：所呈交的毕业设计（论文），是我个人在指导教师的指导下进行的研究工作及取得的成果。
尽我所知，除文中特别加以标注和致谢的地方外，不包含其他人或组织已经发表或公布过的研究成果，也不包含我为获得及其它教育机构的学位或学历而使用过的材料。
对本研究提供过帮助和做出过贡献的个人或集体，均已在文中作了明确的说明并表示了谢意。
作者签名：日期：指导教师签名：日期：使用授权说明本人完全了解大学关于收集、保存、使用毕业设计（论文）的规定，即：按照学校要求提交毕业设计（论文）的印刷本和电子版本；学校有权保存毕业设计（论文）的印刷本和电子版，并提供目录检索与阅览服务；学校可以采用影印、缩印、数字化或其它复制手段保存论文；在不以赢利为目的前提下，学校可以公布论文的部分或全部内容。
作者签名：日期：学位论文原创性声明本人郑重声明：所呈交的论文是本人在导师的指导下独立进行研究所取得的研究成果。
除了文中特别加以标注引用的内容外，本论文不包含任何其他个人或集体已经发表或撰写的成果作品。
对本文的研究做出重要贡献的个人和集体，均已在文中以明确方式标明。
本人完全意识到本声明的法律后果由本人承担。
作者签名：日期：年月日学位论文版权使用授权书本学位论文作者完全了解学校有关保留、使用学位论文的规定，同意学校保留并向国家有关部门或机构送交论文的复印件和电子版，允许论文被查阅和借阅。
本人授权大学可以将本学位论文的全部或部分内容编入有关数据库进行检索，可以采用影印、缩印或扫描等复制手段保存和汇编本学位论文。
涉密论文按学校规定处理。
作者签名：日期：年月日导师签名：日期：年月日注意事项1.设计（论文）的内容包括：1）封面（按教务处制定的标准封面格式制作）2）原创性声明3）中文摘要（300字左右）、关键词4）外文摘要、关键词5）目次页（附件不统一编入）6）论文主体部分：引言（或绪论）、正文、结论7）参考文献8）致谢9）附录（对论文支持必要时）2.论文字数要求：理工类设计（论文）正文字数不少于1万字（不包括图纸、程序清单等），文科类论文正文字数不少于1.2万字。
车牌识别系统算法的研究与实现(小论文).doc
基于图像处理的汽车牌照的识别作者:陈秋菊指导老师:李方洲(温州师范学院物理与电子信息学院 325027)摘要：以一幅汽车牌照的识别为例，具体介绍了车牌自动识别的原理。
整个处理过程分为预处理、边缘提取、车牌定位、字符分割、字符识别五大模块，用MATLAB软件编程来实现每一个部分,最后识别出汽车牌照。
在研究的同时对其中出现的问题进行了具体分析，处理。
寻找出对于具体的汽车牌照识别过程的最好的方法。
关键词：汽车牌照车牌提取字符分割字符识别The vehicle license recognition based on the image processingAuthor:Chen QiujuTutor:Li Fangzhou(School of Physics and Electronic Information Wen Zhou Normal College 325027) Abstract：With one vehicle license recognition, the principle of the automobile License recognition is introduced .This process was divided into pre-process,edge extraction, vehicle license location, character division and character recognition, which is implemented separated by using MATLAB. The license is recognized at last. At the same time, the problems are also analyzed And solved in the process. The best method of recognition to the very vehicle license is found.Keywords: vehicle license vehicle license location character segmentationCharacter recognition1.引言1.1 选题意义汽车牌照自动识别系统是以汽车牌照为特定目标的专用计算机视觉系统，是计算机视觉和模式识别技术在智能交通领域应用的重要研究课题之一，是实现交通管理智能化的重要环节，它可广泛应用于交通流量检测，交通控制与诱导，机场、港口、小区的车辆管理，不停车自动收费，闯红灯等违章车辆监控以及车辆安全防盗等领域，具有广阔的应用前景。
车牌识别系统论文
摘要车牌识别系统（LPRS）是智能交通系统的重要组成部分。
随着机动车辆数量的大幅度增加以及计算机技术的发展，人们对交通控制系统的要求显著提高。
因而智能交通系统被广泛地应用于交通控制系统当中，比如高速公路收费、停车场车辆管理、违章车辆监控、交通诱导控制等场合。
这使得车牌识别系统也得到了更广泛的关注。
与传统的车辆管理方法比较，车牌识别系统可以大大提高交通管理的效率和水平，帮助实现车辆管理的规范化。
本文主要介绍了基于MFC开发的有关数字图像处理的车牌数字识别系统。
系统是利用单张包含车牌的静态图片进行识别的，整个识别过程主要分为车牌定位和字符分割和字符识别三个大的模块。
而其中的字符识别是系统的核心部分。
字符识别目前运用的最多的就是神经网络和模板匹配的方法，本文所介绍的就是基于模板匹配的方法来实现车牌数字的识别。
过程中也相应结合了特征提取、直方图统计等一系列方法。
从实验得知，这种模板匹配的方法实现简单，且容易理解，在确保识别准确率的前提下，可以提高识别的效率，使得系统在比较准确地定位了车牌及分割出字符后，能更准确地实现字符的识别。
关键字：车牌识别；MFC；模板匹配；特征提取AbstractLicense Plate Recognition System (LPRS) is the important part of Intelligent Transportation System. With the increase in the number of motor vehicles and the development of computer technology, the requirements for traffic control systems are significantly increased to people. Because Intelligent Transportation System is widely used in traffic control systems, such as highway tolling, parking vehicles’ management, Illegal vehicles monitoring, traffic guidance and control and so on. So it makes the license Plate Recognition System has also been a more widespread concern. Compared to the traditional methods of vehicle management, license Plate Recognition System can greatly improve the efficiency and level of traffic management to help achieve the standardization of vehicle management.This paper mainly introduces the license Plate Number Recognition System which based on MFC and digital image processing. The system uses static images which contains a plate to recognize the numbers of the plates, the entire recognition process consists of three major modules, license plate location and character segmentation and character recognition. Character recognition is the core of the system. Neural network and template matching are mostly used in Character recognition currently, The Character recognition process introduced in this paper is based on template matching method, it also uses the feature extraction, Histogram statistics and a series of methods. From the experimental results, this method is simple and easy to understand, it can improve the efficiency of recognition , and ensure the accuracy of the recognition at the same time. When the system accurately locates the license plate and segments the characters, the method can recognize the characters accurately.Key word: License Plate Recognition; MFC; Template matching; Feature extraction目录1 绪论 (1)1.1研究的意义及目的 (1)1.2研究的现状及内容 (1)2 相关知识与技术 (3)2.1数字图像处理概述 (3)2.1.1 数字图像的存储和显示 (3)2.1.2 数字图像的处理 (3)2.2图像预处理相关技术 (3)2.2.1 图像灰度化技术 (3)2.2.2 边缘检测技术 (4)2.2.3 图像二值化技术 (7)2.3特征提取技术 (7)2.3.1 纹理特征提取技术 (7)2.3.2 形状和结构特征提取技术 (8)2.4图像分割技术 (8)2.5字符识别技术 (8)2.5.1 字符归一化技术 (8)2.5.2 改进的OPTA细化算法 (9)2.5.3 模板匹配 (10)2.6本章小结 (11)3 车牌数字识别系统的设计与实现 (12)3.1设计目标 (12)3.2系统分析 (12)3.3系统数据结构的设计 (12)3.4系统功能设计 (14)3.4.1 图片预处理功能 (14)3.4.2 车牌搜索与定位的实现 (14)3.4.3 字符分割算法设计 (15)3.4.4 字符归一化思想 (15)3.4.5 字符细化 (16)3.4.6 字符识别过程设计 (16)3.5本章小结 (16)4 系统实现与测试 (17)4.1系统开发环境与工具 (17)4.2实验结果 (17)4.2.1 打开车牌图片 (17)4.2.2 图片预处理 (17)4.2.3 车牌定位 (20)4.2.4 字符分割 (21)4.2.5 字符归一化和细化 (22)4.2.6 字符识别 (23)4.2.7 一键识别 (23)4.3本章小结 (24)5 结论 (25)5.1总结 (25)5.2展望 (25)参考文献 (26)致谢 ........................................................................................................... 错误!未定义书签。
毕业设计BP神经网络方法对车牌照字符的识别(含外文翻译) (1)
摘要为了对车牌字符的识别，本文将BP神经网络应用于汽车车牌的自动识别，在车牌图像进行预处理后的基础上，重点讨论了用BP神经网络方法对车牌照字符的识别。
首先将训练样本做图像预处理，对车牌上的字符进行分割，得到单个字符。
对大小不一的字符做归一化后，对字符进行特征提取，把长为15，宽为25的归一化后的图像中的字符信息提取出来，图像中白点置为0，图像中的黑点置为1，这样就得到了15×25的特征向量，这个特征向量记录的就是字符的特征。
把这个特征向量送到BP网络中进行训练，得到了训练好的权值，把他保存到“win.dat”和“whi.dat”中。
然后打开要识别的图片（即车牌），对图像进行预处理后就可以识别了。
识别率也在90%以上，表明该方法的有效性。
关键字：车牌识别；LPR；字符识别；特征提取； BP神经网络；AbstractFor the discernment to the number plate character, this text applies BP neural network to the automatic discernment of the automobile number plate, on the basis that the number plate picture goes on in advance treated , is it use BP neural network method to car discernment , license plate of character to discuss especially. Will train samples to do the pretreatment of the picture at first, character in number plate cut apart, get the individual character. After making normalization to the character not of uniform size, drew the characteristic to the character 15, wide to draw out for character information of 25 picture behind the normalization, picture white point it puts to be 0, black point of picture is it as 1 , receive 15* 25 characteristic vector quantity like this to put, what the vector quantity of this characteristic is written down is the characteristic of the character . Send the characteristic vector quantity BP network train, get good right value of training, keep him in win.dat and whi.dat. Open picture (namely number plate) discerned to want, go on to picture in advance treated to can discern. The discerning rate is above 90% too; show the validity of this method.Key word：The number plate discerning；The character discerning；LPR；The characteristic is drawn；BP neural network；目录摘要 (Ⅰ)ABSTRACT (Ⅱ)第一章概述 (1)1.1 基本概念 (1)1.2 字符识别简介 (2)1.2.1字符识别发展概况 (2)1.2.2字符识别系统用到的方法 (3)1.2.3字符识别原理 (4)1.3 国内外研究现状和发展趋势 (5)1.4 基于神经网络的字符识别系统 (6)1.4.1 系统简介 (6)1.4.2 系统的基本技术要求 (7)1.4.3系统的软硬件平台 (7)第二章字符识别系统中的关键技术 (8)2.1 特征提取 (8)2.1.1 基本概念 (8)2.1.2 区域内部的数字特征 (10)2.1.3 基于边界的形状特征 (13)2.2 神经网络 (18)2.2.1 人工神经元 (18)2.2.2 人工神经网络构成 (22)2.2.3 人工神经网络的学习规则 (23)2.2.4 BP神经网络 (24)第三章系统的实现 (31)3.1 系统流程图 (31)3.2 程序实现 (31)3.3 程序的总体框架 (36)第四章系统使用说明、测试及注意事项 (37)4.1 系统使用说明 (37)4.2 系统测试 (39)4.2.1 数字识别 (39)4.2.2 字母识别 (40)4.2.3 汉字识别 (40)4.2.4 车牌识别 (41)4.3 注意事项 (41)第五章结论和展望 (42)致谢 (43)参考文献 (44)外文原文与译文 (46)●外文原文 (46)●译文 (57)第一章概述1.1 基本概念随着21世纪经济全球化和信息时代的到来，计算机技术、通信技术和计算机网络技术迅猛发展，自动化的信息处理能力和水平不断提高，并在人们社会活动和生活的各个领域得到广泛应用。
(完整版)车牌识别系统的设计
车牌识别系统的设计1．摘要：汽车牌照自动识别系统是制约道路交通智能化的重要因素,包括车牌定位、字符分割和字符识别三个主要部分。
本文首先确定车辆牌照在原始图像中的水平位置和垂直位置,从而定位车辆牌照,然后采用局部投影进行字符分割。
在字符识别部分,提出了在无特征提取情况下基于支持向量机的车牌字符识别方法。
实验结果表明,本文提出的方法具有良好的识别性能。
随着公路逐渐普及，我国的公路交通事业发展迅速，所以人工管理方式已经不能满着实际的需要，微电子、通信和计算机技术在交通领域的应用极大地提高了交通管理效率。
汽车牌照的自动识别技术已经得到了广泛应用。
2．设计目的：1、使学生在巩固理论课上知识的同时，加强实践能力的提高，理论联系实践。
2、激发学生的研究潜能，提高学生的协作精神，锻炼学生的动手能力。
3．设计原理由于车辆牌照是机动车唯一的管理标识符号，在交通管理中具有不可替代的作用，因此车辆牌照识别系统应具有很高的识别正确率，对环境光照条件、拍摄位置和车辆行驶速度等因素的影响应有较大的容阈，并且要求满足实时性要求。
图1 牌照识别系统原理图该系统是计算机图像处理与字符识别技术在智能化交通管理系统中的应用，它主要由图像的采集和预处理、牌照区域的定位和提取、牌照字符的分割和识别等几个部分组成，如图1 所示。
其基本工作过程如下：（1）当行驶的车辆经过时，触发埋设在固定位置的传感器，系统被唤醒处于工作状态；一旦连接摄像头光快门的光电传感器被触发，设置在车辆前方、后方和侧面的相机同时拍摄下车辆图像；（2）由摄像机或CCD 摄像头拍摄的含有车辆牌照的图像通视频卡输入计算机进行预处理，图像预处理包括图像转换、图像增强、滤波和水平较正等；（3）由检索模块进行牌照搜索与检测，定位并分割出包含牌照字符号码的矩形区域；（4）对牌照字符进行二值化并分割出单个字符，经归一化后输入字符识别系统进行识别。
4．详细设计步骤4.1 提出总体设计方案。
车牌识别英文文献2翻译
实时车辆的车牌识别系统摘要本文中阐述的是一个简炼的用于车牌识别系统的算法。
基于模式匹配，该算法可以应用于对车牌实时检测数据采集，测绘或一些特定应用目的。
拟议的系统原型已经使用C++和实验结果已证明认可阿尔伯塔车牌。
1.介绍车辆的车牌识别系统已经成为在视频监控领域中一个特殊的热门领域超过10年左右。
随着先进的用于交通管理应用的视频车辆检测系统的的到来，车牌识别系统被发现可以适合用在相当多的领域内，并非只是控制访问点或收费停车场。
现在它可以被集成到视频车辆检测系统，该系统通常安装在需要的地方用于十字路口控制，交通监控等，以确定该车辆是否违反交通法规或找到被盗车辆。
一些用于识别车牌的技术到目前为止有如BAM（双向联想回忆）神经网络字符识别[1]，模式匹配[2]等技术。
应用于系统的技术是基于模式匹配，该系统快速，准确足以在相应的请求时间内完成，更重要的是在于阿尔伯塔车牌识别在字母和数字方位确认上的优先发展。
由于车牌号码的字体和方位因国家/州/省份的不同而不同，该算法需要作相应的修改保持其结构完整，如果我们想请求系统识别这些地方的车牌。
本文其余部分的组织如下：第2节探讨了在识别过程中涉及的系统的结构和步骤，第3节解释了算法对于车牌号码的实时检测，第4节为实验结果，第5节总结了全文包括致谢和参考文献。
2.系统架构系统将被用来作为十字路口的交通视频监控摄像系统一个组成部分来进行分析。
图1显示了卡尔加里一个典型的交叉口。
只有一个车牌用在艾伯塔，连接到背面的车辆照相机将被用于跟踪此背面车牌。
图1 卡尔加里一个的典型交叉口系统架构包含三个相异部分：室外部分，室内部分和通信链路。
室外部分是安装摄像头在拍摄图像的不同需要的路口。
室内部分是中央控制站，从所有这些安装摄像头中，接收，存储和分析所拍摄图像。
通信链路就是高速电缆或光纤连接到所有这些相机中央控制站。
几乎所有的算法的开发程度迄今按以下类似的步骤进行。
一般的7个处理步骤已被确定为所有号牌识别算法[3] 共有。
车牌识别系统——车辆牌照定位系统的设计与实现
车牌识别系统——车辆牌照定位系统的设计与实现中文摘要车牌识别系统作为智能交通系统的一个重要组成部分，在交通监控中占有很重要的地位。
车牌识别系统可分为图像预处理、车牌定位、字符识别3个部分，其中车牌定位作为获得车辆牌照图像的重要步骤，是后续的字符识别部分能否正确识别车牌字符的关键环节。
车牌定位系统实现对车辆牌照进行定位的功能,即从包含整个车辆的图像中找到车牌区域的位置，并对该车牌区域进行定位显示，将定位信息提供给字符识别部分。
在本文中作者分析出车辆牌照具有如下特征：（1）具有固定的长宽比；（2）车牌区域内部字符数目固定；（3）字符与背景之间存在很大的颜色差别；（4）对于含有车牌信息的灰度图像，其车牌区域边缘明显，灰度跳变大，相对于车牌以外区域，具有明显的特征等。
所以，一般基于图像处理的车牌定位系统是通过分析车辆牌照的某些特征来进行定位的。
针对车牌本身固有的特征，本文首先介绍了在车牌定位过程中常用的几种数字图像处理技术：图像的二值化处理、边缘检测和图像增强等。
其次介绍了现在常用的车牌定位方法，并对这些方法进行分析，总结出各种方法的优缺点，然后在此基础上提出采用带边缘检测的灰度图像行扫描投影方法对车牌进行定位，并使用VC++6.0编码实现车牌定位系统。
最后对该系统进行了测试，测试结果表明该系统具有良好的人机交互方式，具有较高的识别正确率和较快的识别速度，对用户给定的待测图像能够迅速准确地进行车辆牌照的定位并将定位结果显示给用户，该系统具有一定的实用价值。
关键词：车牌定位；灰度图像；行扫描；投影License Plate Recognition System - - Design and Implementation of vehiclelicense Positioning SystemABSTRACTAs an important part of the Intelligent Transportation Systems, License Plate Recognition System plays an important role in traffic monitoring area. License plate recognition system can be divided into three parts, i.e., image pre-processing, license plate location and character recognition. The vehicle license plate location is an important procedure which is used to obtain a license image. It is also the key of the following character recognition system which can identify the correct license plate characters. License plate location system can perform the vehicle license location function, i.e., finding the location of the vehicle license in the image containing the entire vehicle license plate, positioning the plate region and then demonstrating the location information on the computer screen which will be transferred to the character recognition system.In this thesis, the author analyzes the vehicle license and finds that it has the following characteristics: (1) Fixed aspect ratio. (2) Fixed license plate characters number. (3) Great color difference between characters and background. (4) Obvious edge and great intensity change for grayscale images with registration information, and obvious characteristics compared with the outer plate region. Therefore, the majority of image-based positioning systems perform location function by analyzing some characteristics of the vehicle license.According to the own inherent characteristics of license plate, this thesis introduces many commonly used digital image processing techniques in the location process of license plate: binary image processing, edge detection and image enhancement, and so on. Then, we introduce the commonly used methods of license plate location. Further, we analysis these methods and summarize their advantages and disadvantages. Moreover, we propose locating plate by using the gray-scale image projection and line scanning method with edge detection. This system was implemented by using the VC++ 6.0. Finally, the experimental results indicate that the system has a good human-computer interaction, a better identification rate and higher speed. For images provided by users, the system can quickly and accurately locate the vehicle license and display the location results to the users. Therefore, this system has some practical values.KEY WORDS: license plate location; gray-scale images; line scan; projection目录中文摘要 (I)Abstract (II)目录 (III)第一章绪论 (1)1.1 课题的来源及意义 (1)1.2 课题主要研究的问题 (1)1.3 系统设计的目标及基本思路 (1)1.3.1 设计目标 (1)1.3.2 基本思路 (1)第二章车牌定位中常用的数字图像处理技术 (3)2.1 汽车牌照的特征 (3)2.2 数字图像处理技术概述 (3)2.3 DIB图像概述 (3)2.4 车牌定位中常用的数字图像处理技术概述 (3)2.4.1 图像二值化 (3)2.4.2 边缘检测 (3)2.4.3 图像增强 (3)第三章车牌定位方法研究.......................................................................... 错误!未定义书签。
车牌识别毕业论文
摘要车牌自动识别技术是实现智能交通系统的关键技术，对我国交通事业的发展起着十分重要的作用，进而影响我国的经济发展速度及人们的生活质量。
车牌识别系统运用模式识别、人工智能技术，能够实时准确地自动识别出车牌的数字、字母及汉字字符，进而实现电脑化监控和管理车辆。
一个车牌识别系统的基本硬件配置有照明装置、摄像机、主控机、采集卡等。
而软件则是由具有车牌识别功能的图像分析和处理软件，以及能够具体满足应用需求的后台管理软件组成。
车牌自动识别系统主要分为图像预处理、车牌定位、字符分割和字符识别等主要模块,也包括后续应用程序的开发。
针对不同的模块,本文研究分析了现有的理论算法,并提出了具有实际应用意义的解决方案。
1.在图像预处理模块,因为人眼对于不同颜色分量的敏感度不同,图像灰度化采用加权平均值法；二值化过程中阈值的选取至关重要,本文采用动态自适应阈值法,效果理想；边缘提取利用了拉普拉斯算子;去噪过程采用的是中值滤波方法；2.车牌定位模块包括粗定位和细定位,本文通过分析车牌的尺寸、类型、颜色,得到不同的特征向量,即车牌的几何特征、灰度分布特征、投影特征和字符排列特征等,利用这些特征进行车牌定位；3.在车牌字符分割模块,提出了双向对比垂直投影分割法,该方法基于车牌的垂直投影,能够将字符准确的分割开,利于车牌字符识别: 4.本文对车牌数字和车牌字母及汉字提出了不同的处理方法,数字识别采用投影技术,汉字和字母识别应用BP神经网络技术,兼顾了识别准确率和识别速度;根据上述方法原理,基于MATLAB软件进行程序设计,编制了车牌自动识别软件。
关键字：车牌图像；图像处理；字符分割；BP神经网络AbstractLicense plate recognition technology is to realize the key technology of intelligent transportation system of our country, the development of the cause of traffic plays a very important role, then affects the economic development of our country and speed and people's quality of life. License plate recognition system with pattern recognition, artificial intelligence technology, to real-time accurately recognize the license plate number of automatic, letters and Chinese characters, and achieve computerized monitoring and management vehicles. A license plate recognition system of basic hardware configuration have lighting devices, video camera, master control machine, acquisition card, etc. And software is with license plate identification function by the image analysis and processing software, and can meet the demand of the specific application background management software component. License plate recognition system mainly divided into the image preprocessing, license plate location, character segment and character recognition and other major modules, including the follow-up application development.In view of the different module, this paper analyzed the existing algorithm theory, and puts forward the practical significance of the solution. 1. In the image preprocessing module, for the human eye to different color the sensitivity of the component is different, the image intensity by weighted average method; In the process of binary of the threshold is very important to select is adopted in this paper, dynamic adaptive threshold value method, the effect ideal; Using the Laplace operator edge extraction; Denoising the process is the median filtering method; 2. The license plate localization module contains coarse position and fine positioning, the paper analyzes the license plate size, type, color, get different characteristic vector, namely the geometrical characteristics of the license plate, gray distribution, projection characteristics and characters arrangement characteristics, use these characteristics of the license plate location; 3. In the license plate character segmentation module, and put forward the two-way contrast vertical projection segmentation method, this method is based on the license plate vertical projection, can make the character of accurate separated, beneficial to the license plate character recognition: 4. This article on license plate Numbers and letters and characters put forward different processing methods, number recognition by projection technology, Chinese characters and letters recognition application BP neural network technology, and taking account of the identification accuracy and recognition rate; According to the above method, based on the MATLAB software program design, compiled the license plate recognition software.Keywords License plate image, image processing, character segment, the BP neural network目录摘要............................................. 错误!未定义书签。
车辆牌照自动识别系统设计及实现
交通监控系统：通过摄像机设备监视各个路段交通流和车里狼的状况，获得道路上的各种信息，如车辆排队队形、对长和密度。观察和记录下交通事故。同时识别系统可以同测速雷达一起配合使用，可以检测出超速的车辆。当发现有车辆违规，通过车辆牌照自动定位系统识别出该车的车牌号，并向该车发送其违规的警告。
1.1
1
从20世纪80年代开始，因为我国经济的迅猛发展，车辆规模和车流量都大幅度上升，全国车量每年大均增高15%，城市车辆行驶里程每年大均增高7.9%，城市和高速交通的现代化管理技术的提高已经势在必行，迫切的需要高科技来加强道路上交通的管理和控制水平。
智能交通管理系统（Intelligent Transport Systems,ITS）是本世纪交通道路管理发展的主要方向，为智能交通管理系统进入实际应用领域提供了契机。在智能交通管理系统中，车牌识别（License Plate Recognition,LPR）系统是实现智能化交通管理的重要环节，其采用数字图像处理技术以及动态目标自动跟踪技术，对图像进行快速实时的自动识别处理 (车牌自动定位、字符分割、字符识别)，是集车辆视频检测、动态模糊消除、车牌自动识别和车辆管理数据库自动检索等先进技术于一体的检测系统。同时采用先进的电脑控制云台镜头智能调节功能，速度快、清晰度高、使用方便。在不影响交通秩序的情况下，能随时随地自动实时检测、识别、检索和记录行进中的车辆车牌。系统在使用时，可以将摄像机放在车顶或路旁，主机与供电系统放在车辆内部，主机对摄像机获得的画面进行处理，自动进行车牌识别并与主机中的数据库进行对比，发现欠费、违章、盗抢、无记录等车辆自动报警，以便及时拦截。主机中的数据库通过移动硬盘、光盘、有线、无线网络及时更新。可以实现远程控制、查询。其具体的应用可概括为：
基于opencv的车牌识别系统设计与实现-毕业论文
---文档均为word文档，下载后可直接编辑使用亦可打印---摘要科技的进步以及人民自身的生活水平的不断提高，使得人们对于日常的出行需求变得不断增长。
汽车作为最常见的交通工具已经越来越成为人们最初的选择。
大量新的车辆在不断地投入到道路中使用，而以传统的人工方式对汽车车辆的管理也变得愈加困难。
因此，使用计算机来代替人来处理相对繁重的工作是必要的。
一个良好的交通管理系统是实现道路管理的基础。
想要对于汽车车辆进行管理，最有效的识别特征之一便是汽车的车牌，作为目前最常见的使用技术，车牌识别广泛应用在交叉路段、停车场、收费站等各种场合的监控与管理之中。
所以需要相应的技术来完成以上的需求。
本文以python为使用语言，OpenCV为主要工具，通过输入带有汽车车牌的图像，根据车牌所特有的一些特征，垂直投影法、SVM的方法来完成对于汽车车辆的车牌定位、车牌的字符分割以及字符识别功能。
最终将所识别到的车牌字符输出显示出来。
关键词：OpenCV；投影法；SVM；车牌识别AbstractThe advancement of science and technology and the continuous improvement of people's own living standards have made people's daily travel needs continue to grow. As the most common mode of transportation, cars have become the initial choice of people. A large number of new vehicles are constantly being put into use on the road, and the management of automobile vehicles by traditional manual methods has become increasingly difficult. Therefore, it is necessary to use a computer instead of a person to handle relatively heavy work. A good traffic management system is the foundation for road management.One of the most effective identification features for the management of automobile vehicles is the license plate of the car. As the most commonly used technology at present, license plate recognition is widely used in monitoring and management of various occasions such as intersections, parking lots, toll stations In. Therefore, corresponding technology is needed to complete the above requirements. This article uses python as the language and OpenCV as the main tool. By inputting an image with a car license plate, according to some characteristics unique to the license plate, vertical projection and SVM are used to complete the license plate positioning, character segmentation and characters of the car license plate. Recognition function. Finally, the recognized license plate characters are displayed.Keywords：OpenCV；SVM；projection method; License Plate Recognition1 绪论1.1选题背景与意义1.1.1选题背景随着人们的生活水平的不断提高以及对日常出行需求的不断增长，汽车成为越来越多人出行所选择的交通工具。
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毕业设计（论文）外文文献翻译
文献、资料中文题目：汽车车牌识别系统-车牌定位子系统
的设计与实现
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翻译日期： 2017.02.14
汽车车牌识别系统
---车牌定位子系统的设计与实现
摘要
汽车车牌识别系统是近几年发展起来的计算机视觉和模式识别技术在智能交通领域应用的重要研究课题之一。
在车牌自动识别系统中，首先要将车牌从所获取的图像中分割出来实现车牌定位，这是进行车牌字符识别的重要步骤，定位的准确与否直接影响车牌识别率。
本次毕业设计首先对车牌识别系统的现状和已有的技术进行了深入的研究，在此基础上设计并开发了一个基于MATLAB的车牌定位系统，通过编写MATLAB文件，对各种车辆图像处理方法进行分析、比较,最终确定了车牌预处理、车牌粗定位和精定位的方法。
本次设计采取的是基于微分的边缘检测，先从经过边缘提取后的车辆图像中提取车牌特征，进行分析处理，从而初步定出车牌的区域，再利用车牌的先验知识和分布特征对车牌区域二值化图像进行处理，从而得到车牌的精确区域，并且取得了较好的定位结果。
关键词：图像采集，图像预处理，边缘检测，二值化，车牌定位
ENGLISH SUBJECT
ABSTRACT
The subject of the automatic recognition of license plate is one of the most significant subjects that are improved from the connection of computer vision and pattern recognition. In LPSR, the first step is for locating the license plate in the captured image which is very important for character recognition. The recognition correction rate of license plate is governed by accurate degree of license plate location.
Firstly, the paper gives a deep research on the status and technique of the plate license recognition system. On the basis of research, a solution of plate license recognition system is proposed through the software MATLAB,by the M-files several of methods in image manipulation are compared and analyzed. The methods based on edge map and das differential analysis is used in the process of the localization of the license plate, extracting the characteristics of the license plate in the car images after being checked up for the edge, and then analyzing and processing until the probably area of license plate is extracted，then come out the resolutions for localization of the car plate.
KEY WORDS：imageacquisition，image preprocessing，edge detection，binarization，licence，license plate location
前言 (1)
第1章绪论 (2)
§1.1 课题研究的背景 (2)
§1.2 车牌的特征 (2)
§1.3 国内外车辆牌照识别技术现状 (3)
§1.4车牌识别技术的应用情况 (4)
§1.5 车牌识别技术的发展趋势 (5)
§1.6车牌定位的意义 (6)
第2章 MATLAB简介 (7)
§2.1 MATLAB发展历史 (7)
§2.2 MATLAB的语言特点 (7)
第3章图像预处理 (10)
§3.1 灰度变换 (10)
§3.2 图像增强 (11)
§3. 3 图像边缘提取及二值化 (13)
§3. 4 形态学滤波 (18)
第4章车牌定位 (21)
§4.1车牌定位的主要方法 (21)
§4.1.1基于直线检测的方法 (22)
§4.1.2 基于阈值化的方法 (22)
§4.1.3 基于灰度边缘检测方法 (22)
§4.1.4 基于彩色图像的车牌定位方法 (25)
§4.2 车牌提取 (26)
结论 (30)
参考文献 (31)
致谢 (33)
随着交通问题的日益严重，智能交通系统应运而生。
从20世纪90年代起，我国也逐渐展开了智能交通系统的研究和开发，探讨在现有的交通运输网的基础上，提高运输效率，保障运输安全。
我国加强智能交通系统（ITS）的研究与开发势在必行，特别是考虑到我国的国情和我国经济的快速发展，社会信息化程度日益提高，交通管理智能化成为发展的趋势。
汽车牌照自动识别系统是近几年发展起来的计算机视觉和模式识别技术在智能交通领域应用的重要研究课题之一。
车牌识别的目的是对摄像头获取的汽车图像进行预处理,确定车牌位置,提取车牌上的字符串，并对这些字符进行识别处理,用文本的形式显示出来。
车牌自动识别技术在智能交通系统中具有重要的应用价值。
在车牌自动识别系统中，首先要将车牌从所获取的图像中分割出来，这是进行车牌字符识别的重要步骤，定位准确与否直接影响车牌识别率。
本次设计主要对车牌的定位做了比较详细的研究。
汽车牌照自动识别系统作为一种交通信息的获取技术在交通车辆管理、园区车辆管理、停车场管理有着特别重要的应用价值，受到业内人士的普遍关注。
车牌自动识别的处理有三部分组成，其中车牌定位作为最关键的技术，成为重点研究的对象。
车牌定位的成功与否以及定位的准确程度将会直接决定后期能否进行车牌识别以及识别的准确度。
由于在现实中，汽车的车牌图像受到光照、背景、车型等外界干扰因素以及拍摄角度、远近等人为因素的影响，造成图像受光不均匀，车牌区域不明显，给车牌区域的提取带来了较大的困难。
车牌定位的方法有很多种，目前比较经典的定位方法大都在基于灰度图像的基础上。
本次毕业设计就针对灰度图像的定位进行了研究。
针对不同背景和光照条件下的车辆图像，提出了一种基于灰度图像灰度变化特征进行车牌定位的方法。
依据车牌中不同区域的灰度分布，车牌定位时可以首先将彩色车牌进行灰度化然后再进行车牌定位。
第1章绪论
§1.1 课题研究的背景
随着21世纪经济全球化的到来，高速度、高效率的生活节奏，使车辆普及成为必然的趋势，交通管理自动化越来越成为亟待解决的问题。
现代智能交通系统 (Intelligent Transportation System，ITS)中，车辆牌照识别(License Plate Recognition，LPR)技术是计算机视觉与模式识别技术在交通领域应用的重要研究课题之一，是实现交通管理能够智能化的重要环节，其任务是分析、处理汽车图像，自动识别汽车牌号。
LPR系统可以广泛应用于电子收费、出入控制、公路流量监控、失窃车辆查询和停车场车辆管理等需要车牌认证的场合；尤其在高速公路收费系统中，实现不停车收费提高公路系统的运行效率，LPR系统更具有不可替代的作用。
因而从事LPR技术的研究具有极其重要的现实意义和巨大的经济价值。
LPR系统中的两个关键子系统是车牌定位系统和车牌字符识别系统。
关于车牌定位系统的研究，国内外学者已经做了大量的工作，但实际效果并不是很理想，比如车牌图像的倾斜、车牌表面的污秽和磨损、光线的干扰等都是影响定位准确度的潜在因素。
为此，近年来不少学者针对车牌本身的特点、车辆拍摄的不良现象及背景复杂状况，先后提出了许多有针对性的定位方法，使车牌定位在技术和方法上都有了很大的改善。
然而现代化交通系统不断提高的快节奏，将对车牌定位的准确率和实时性提出更高的要求，因而进一步加深车牌定位的研究是非常有必要的。
§1.2 车牌的特征
车牌的本身具有许多固有特征，这些特征对不同的国家是不同的，我国现在使用的车牌主要根据中华人民共和国机动车牌号GA36-92标准，具有以下特征：
（1）形状特征：标准的车牌外轮廓尺寸440*140，字符高90，宽45，字符间距12，间隔符宽10。
整个字符的高宽比例近似为3:1，车牌的边缘是线段围成的有规则的矩形。
主要用在车牌的定位分割。
（2）颜色特征：现有的字符颜色与车牌底色搭配有四种类型，蓝底白字，黄底黑字，白底黑字，黑底白字。
这部分特征主要用在对彩色图像进行车牌的定位。