车牌定位摘要及翻译
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。
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车辆牌照定位算法研究基于颜色特征和板游行杨耀权，白洁，田瑞丽，和南柳控制科学与工程学院，华北电力大学，保定071003，中国yyq2201@摘要。
定位地区牌照的关键部件是牌照识别系统。
一种新的方法，本文采用取代传统的方法是基于灰度图像。
该方法充分利用颜色特征的彩色图像是基于颜色搭配的板的背景和人物结合板的结构和纹理查找车辆牌照。
板的区域，然后将修订值。
定位率达98%的实验。
1简介智能交通系统的主要发展方向的技术和交通管理，车辆牌照自动识别是关键的组成部分这一系统。
它起着重要的作用在管理城市街道，港口，机场，公路和停车场。
在车辆牌照识别系统，车牌定位系统是最重要的一部分。
在过去，有限的速度和记忆能力的处理器，车牌识别主要是基于灰度图像识别中的车牌实时。
但缺陷的灰度图像处理技术是其错误率高，当对比度低、亮度是不平等的，或其他一些区域的结构和纹理相似的板的区域。
近年来，计算机的性能有了显著的提高，和现在可以处理大量的颜色信息的迅速和有效。
如今，越来越多的学者来定位车牌区域的彩色图像处理技术。
这些方法主要有如下：1。
首先，分割图像中的颜色使用的神经网络。
其次，利用垂直和水平投影来计算板的背景颜色部分。
然后，该板块的地区是位于使用比板的高度和宽度[ 1]。
2。
基于灰度图像，模板匹配是用来找到角点的板。
如果有四种可能的角点被发现，内容的四边形是检查其空间频率。
某些空间频率预计由于人物在板。
只有在的情况下，这个频率内容确认其存在，是四个角点被接受为角点牌照[ 2]。
3。
处理图像边缘的距离空间和颜色相似，然后分割灰度图像的候选车牌区域的纹理定位板[ 3]。
影响板的位置是改善这些方法，但当亮度变化或板是倾斜的图像，定位误差率很高。
如果想增加可靠性，那么应该充分利用了颜色信息的彩色图像。
有一个重要的属性，中国汽车板：有四片背景颜色，蓝色，黑色，白色和黄色。
有三种颜色使用的号牌字符：黑色，红色和白色。
固定的搭配。
这个属性，我们可以快速有效地定位车牌区域。
汽车车牌定位识别概述
汽车车牌定位识别概述汽车车牌定位识别技术的发展得益于计算机视觉技术的进步和硬件设备的不断更新。
自从20世纪80年代末期开始，随着计算机技术的发展，人们开始研究如何利用计算机自动识别车牌。
最初的方法是通过车牌字符的特征提取和模式匹配来实现，但是这种方法在实际应用中存在一些问题，比如对于光照条件、角度和车辆速度的不同会导致识别结果的准确度下降。
随着深度学习技术的兴起，特别是卷积神经网络（Convolutional Neural Network，CNN）的发展，汽车车牌定位识别技术得到了显著的进步。
CNN可以通过学习大量的车牌图像来自动提取图像特征，并通过训练模型来识别不同类型的车牌。
这种方法不仅可以提高识别的准确性，还可以适应不同的光照和角度条件。
汽车车牌定位识别技术的应用非常广泛。
首先，在交通安全领域，汽车车牌定位识别可以帮助交警自动检测和记录违反交通规则的车辆，比如闯红灯、超速等。
这种技术可以大大提高交通管理的效率和准确性，减少人为差错。
其次，在停车场管理中，汽车车牌定位识别可以帮助自动识别道闸前的车牌信息，实现自动出入场的管理。
这不仅方便了车辆的出入，还可以提高停车场的管理效率。
另外，在安防领域，汽车车牌定位识别可以帮助监控系统自动追踪和识别特定车辆的位置和行动轨迹，有助于犯罪侦查和预防。
汽车车牌定位识别技术通常包括以下几个步骤。
首先，对车辆图像进行预处理，包括图像去噪、图像增强等。
然后，利用目标检测算法来定位车牌的位置，常用的方法包括边缘检测、颜色分割等。
接下来，对定位到的车牌进行字符分割，将车牌中的字符单独分离出来。
最后，利用字符识别算法对分割后的字符进行识别，常见的方法包括模板匹配、字符特征提取等。
虽然汽车车牌定位识别技术已经取得了很大的进展，但是在实际应用中仍然存在一些挑战。
首先，不同车牌的形状和颜色差异较大，车牌的角度和光照条件也会导致识别的准确性下降。
其次，特定地区的车牌字符种类较多，字符的形状和位置也有差异，这对识别算法提出了更高的要求。
车牌定位
摘要: 车牌定位是车牌自动识别技术中的一个关键问题，许多学者研究发展多种车牌定位算法。
简要介绍和比较了目前比较常见的几种车牌定位方法进行了。
车牌识别LPR(License Plate Recogniti ON)技术作为交通管理自动化的重要手段和车辆检测系统的一个重要环节，能经过图像抓拍、车牌定位、图像处理、字符分割、字符识别等一系列算法运算，识别出视野范围内的车辆牌照号码；它运用数字图像处理、模式识别、人工智能技术对采集到的汽车图像进行处理，能够实时准确地自动识别出车牌的数字、字母及汉字字符，并以计算机可直接运行的数据形式给出识别结果，使得车辆的电脑化监控和管理成为现实。
车牌识别技术的任务是处理、分析摄取的视频流中复杂背景的车辆图像，定位、分割牌照字符，最后自动识别牌照上的字符。
为了保证汽车车牌识别系统能在各种复杂环境下发挥其应有的作用，识别系统必须满足以下要求:(1)鲁棒性:在任何情况下均能可靠正常地工作，且有较高的正确识别率。
(2)实时性:不论在汽车静止还是高速运行情况下，图像的采集识别系统必须在一定时间内识别出车牌全部字符，达到实时识别。
车牌识别技术的关键在于车牌定位、字符分割和字符识别三部分，其中车牌定位的准确与否直接决定后面的字符分割和识别效果，是影响整个LPR系统识别率的主要因素，是车牌识别技术中最为关键的一步。
目前车牌定位的方法多种多样, 归纳起来主要有基于纹理特征分析的方法、基于边缘检测的方法、基于数学形态学定位、基于小波分析定位以及基于彩色图像定位等，这些方法各有所长。
1、车牌目标区域特点车牌定位方法的出发点是利用车牌区域的特征来判断牌照，将车牌区域从整幅车辆图像中分割出来。
车牌自身具有许多的固有特征，这些特征对于不同的国家是不同的。
从人的视觉角度出发，我国车牌具有以下可用于定位的特征:(1)车牌底色一般与车身颜色、字符颜色有较大差异;(2)车牌有一个连续或由于磨损而不连续的边框;(3)车牌内字符有多个，基本呈水平排列，在牌照的矩形区域内存在丰富的边缘，呈现规则的纹理特征;(4)车牌内字符之间的间隔较均匀，字符和牌照底色在灰度值上存在较大的跳变，字符本身和牌照底内部都有比较均匀的灰度;(5)不同图像中牌照的具体大小、位置不确定，但其长宽比在一定的变化范围内，存在1个最大值和1个最小值。
车牌定位方法探讨
电源管理：Full featured －Sipports Run ，Idle and Sle－ep modes复位：设置复位开关电池：3．7V 锂离子电池物理尺寸：主板尺寸为65x53x5．5mm 电源适配器：5V 直流外部连接器：Possible integration 、CF 卡、Sensor 、Blue－tooth 、SIM Card 、其他3．2存储器系统存储器的物理实质是一组或多组具备数据输入输出和数据存储功能的集成电路，用于充当设备缓存或保存固定的程序及数据。
3．3电源电源部分的关键问题是低功耗的设计问题，低功耗的措施一般有：降低电压、降低时钟频率、选择低功耗器件等等。
本系统电源的提供有电池供电和市电供电两种。
电池供电功耗低、供电稳定、扛干扰能力好，但峰值性能不好。
市电供电要有相应的AC－DC 的适配器，并在系统上配置相应的线性稳压器进行DC－DC 转换。
3．4服务程序的设计智能手机系统中的服务程序主要有GUI Server 和GSM ／GPRS Server ，都是上层应用赖以实现的基础。
为使系统能够很好地支持浏览器及MMS 等界面复杂的应用，具有良好的可扩展性，系统中的GUI Server 设计采用了客户机／服务器模式。
服务进程与应用进程之间采用Linux 提供的消息队列进行通信。
服务进程保存系统GUI 环境的描述信息，为应用进程提供注册及一些计算任务，如计算当前剪切域内容等。
此外，还负责显示桌面。
4结束语随着智能手机的销量上升，设计者应将目光投向内置的可编程图形、增长的处理功能及通信选项，以替换或增强最具挑战性和最昂贵的嵌入系统部件之一：用户界面。
经过正确的设置，在便携智能手机上点击几下，就能连接到并管理任何嵌入设备。
智能手机作为嵌入设备的控制器，还可以有多种应用，如工业控制器、门禁控制产品、医疗仪器、安保系统、环境控制，甚至家居自动化设备，因此智能手机的开发应用有着广阔的发展前景。
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。
车牌识别外文翻译
中英文翻译A configurable method for multi-style license platerecognitionAutomatic license plate recognition (LPR) has been a practical technique in the past decades. Numerous applications, such as automatic toll collection, criminal pursuit and traffic law enforcement , have been benefited from it . Although some novel techniques, for example RFID (radio frequency identification), WSN (wireless sensor network), etc., have been proposed for car ID identification, LPR on image data is still an indispensable technique in current intelligent transportation systems for its convenience and low cost. LPR is generally divided into three steps: license plate detection, character segmentation and character recognition. The detection step roughly classifies LP and non-LP regions, the segmentation step separates the symbols/characters from each other in one LP so that only accurate outline of each image block of characters is left for the recognition, and the recognition step finally converts greylevel image block into characters/symbols by predefined recognition models. Although LPR technique has a long research history, it is still driven forward by various arising demands, the most frequent one of which is the variation of LP styles, for example:(1) Appearance variation caused by the change of image capturingconditions.(2)Style variation from one nation to another.(3)Style variation when the government releases new LP format. Wesummed them up into four factors, namely rotation angle,line number, character type and format, after comprehensive analyses of multi-style LP characteristics on real data. Generally speaking, any change of the above four factors can result in the change of LP style or appearance and then affect the detection, segmentation or recognition algorithms. If one LP has a large rotation angle, the segmentation and recognition algorithms for horizontal LP may not work. If there are more than one character lines in one LP, additional line separation algorithm is needed before a segmentation process. With the variation of character types when we apply the method from one nation to another, the ability to re-define the recognition models is needed. What is more, the change of LP styles requires the method to adjust by itself so that the segmented and recognized character candidates can match best with an LP format.Several methods have been proposed for multi-national LPs or multiformat LPs in the past years while few of them comprehensively address the style adaptation problem in terms of the abovementioned factors. Some of them only claim the ability of processing multinational LPs by redefining the detection and segmentation rules or recognition models.In this paper, we propose a configurable LPR method which is adaptable from one style to another, particularly from one nation to another, by defining the four factors as parameters.1Users can constrain the scope of a parameter and at the same time the method will adjust itself so that the recognition can be faster and more accurate. Similar to existing LPR techniques, we also provide details of detection, segmentation and recognition algorithms. The difference is that we emphasize on the configurable framework for LPR and the extensibility of the proposed method for multistyle LPs instead of the performance of each algorithm.In the past decades, many methods have been proposed for LPR that contains detection, segmentation and recognition algorithms. In the following paragraphs, these algorithms and LPR methods based on them are briefly reviewed.LP detection algorithms can be mainly classified into three classes according to the features used, namely edgebased algorithms, colorbased algorithms and texture-based algorithms. The most commonly used method for LP detection is certainly the combinations of edge detection and mathematical morphology .In these methods, gradient (edges) is first extracted from the image and then a spatial analysis by morphology is applied to connect the edges into LP regions. Another way is counting edges on the image rows to find out regions of dense edges or to describe the dense edges in LP regions by a Hough transformation .Edge analysis is the most straightforward method with low computation complexity and good extensibility. Compared with edgebased algorithms, colorbased algorithms depend more on the application conditions. Since LPs in a nation often have several2predefined colors, researchers have defined color models to segment region of interests as the LP regions .This kind of method can be affected a lot by lighting conditions. To win both high recall and low false positive rates, texture classification has been used for LP detection. In Ref.Kim et al. used an SVM to train texture classifiers to detect image block that contains LP pixels.In Ref. the authors used Gabor filters to extract texture features in multiscales and multiorientations to describe the texture properties of LP regions. In Ref. Zhang used X and Y derivative features,grey-value variance and Adaboost classifier to classify LP and non-LP regions in an image.In Refs. wavelet feature analysis is applied to identify LP regions. Despite the good performance of these methods the computation complexity will limit their usability. In addition, texture-based algorithms may be affected by multi-lingual factors.Multi-line LP segmentation algorithms can also be classified into three classes, namely algorithms based on projection，binarization and global optimization. In the projection algorithms, gradient or color projection on vertical orientation will be calculated at first. The “valleys”on the projection result are regarded as the space between characters and used to segment characters from each other.Segmented regions are further processed by vertical projection to obtain precise bounding boxes of the LP characters. Since simple segmentation methods are easily affected by the rotation of LP, segmenting the skewed LP becomes a key issue to be solved. In the binarization algorithms, global or local methods are often used3to obtain foreground from background and then region connection operation is used to obtain character regions. In the most recent work, local threshold determination and slide window technique are developed to improve the segmentation performance. In the global optimization algorithms, the goal is not to obtain good segmentation result for independent characters but to obtain a compromise of character spatial arrangement and single character recognition result. Hidden Markov chain has been used to formulate the dynamic segmentation of characters in LP. The advantage of the algorithm is that the global optimization will improve the robustness to noise. And the disadvantage is that precise format definition is necessary before a segmentation process.Character and symbol recognition algorithms in LPR can be categorized into learning-based ones and template matching ones. For the former one, artificial neural network (ANN) is the mostly used method since it is proved to be able to obtain very good recognition result given a large training set. An important factor in training an ANN recognition model for LP is to build reasonable network structure with good features. SVM-based method is also adopted in LPR to obtain good recognition performance with even few training samples. Recently, cascade classifier method is also used for LP recognition. Template matching is another widely used algorithm. Generally, researchers need to build template images by hand for the LP characters and symbols. They can assign larger weights for the important points, for example, the corner points, in the4template to emphasize the different characteristics of the characters. Invariance of feature points is also considered in the template matching method to improve the robustness. The disadvantage is that it is difficult to define new template by the users who have no professional knowledge on pattern recognition, which will restrict the application of the algorithm.Based on the abovementioned algorithms, lots of LPR methods have been developed. However, these methods aremainly developed for specific nation or special LP formats. In Ref. the authors focus on recognizing Greek LPs by proposing new segmentation and recognition algorithms. The characters on LPs are alphanumerics with several fixed formats. In Ref. Zhang et al. developed a learning-based method for LP detection and character recognition. Their method is mainly for LPs of Korean styles. In Ref. optical character recognition (OCR) technique are integrated into LPR to develop general LPR method, while the performance of OCR may drop when facing LPs of poor image quality since it is difficult to discriminate real character from candidates without format supervision. This method can only select candidates of best recognition results as LP characters without recovery process. Wang et al. developed a method to recognize LPR with various viewing angles. Skew factor is considered in their method. In Ref. the authors proposed an automatic LPR method which can treat the cases of changes of illumination, vehicle speed, routes and backgrounds, which was realized by developing new detection and segmentation algorithms with robustness to the5illumination and image blurring. The performance of the method is encouraging while the authors do not present the recognition result in multination or multistyle conditions. In Ref. the authors propose an LPR method in multinational environment with character segmentation and format independent recognition. Since no recognition information is used in character segmentation, false segmented characters from background noise may be produced. What is more, the recognition method is not a learning-based method, which will limit its extensibility. In Ref. Mecocci et al. propose a generative recognition method. Generative models (GM) are proposed to produce many synthetic characters whose statistical variability is equivalent (for each class) to that showed by real samples. Thus a suitable statistical description of a large set of characters can be obtained by using only a limited set of images. As a result, the extension ability of character recognition is improved. This method mainly concerns the character recognition extensibility instead of whole LPR method.From the review we can see that LPR method in multistyle LPR with multinational application is not fully considered. Lots of existing LPR methods can work very well in a special application condition while the performance will drop sharply when they are extended from one condition to another, or from several styles to others.多类型车牌识别配置的方法自动车牌识别（LPR）在过去的几十年中的实用技术。
汽车车牌识别系统毕业论文(带外文翻译)解析
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目录前言 (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)前言随着交通问题的日益严重，智能交通系统应运而生。
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。
中英文中英文文献翻译-对车牌定位的研究
(2000) devised a method based on vector quantization (VQ). VQ image representation is a quadtree representation by the specific coding mechanism,and it can give a system some hints about the contents of image regions, and such information boosts location performance. Park et al. (1999)used neural networks to locate license plate. Neural networks can be used as filters for analyzing small windows of an image and deciding whether each window contains a license plate, and their inputs are HSI values; a post-processor combinesthese filtered images and locates the bounding boxes of license plates in the image. Besides neural networks, other filters have been considered too. For example, some authors used line sensitive filters to extract the plate areas. License plates are identified as image areas with high density of rather thin dark lines or curves. Therefore, localization is handled looking for rectangular regions in the image containing maxima of response to these line filters, which is computed by a cumulative function (Luis et al., 1999). Plate characters can be direct identified by scanning through the input image and looking for portions of the image that were not linked to other parts of the image.If a number of characters are found to be in a straight line, they may make up a license plate (Lim et al., 1998). Fuzzy logic has been applied to the problem of locating license plate by Zimic et al. (1997). The authors made some intuitive rules to describe the license plate, and gave some membership functions for the fuzzy sets ‘‘bright’’ and ‘‘dark’’, ‘‘bright and dark sequence’’ to getthe horizontal and vertical plate positions. But this method is sensitive to the license plate color and brightness and needs much processing time. Using color features to locate license plate has been studied by Zhu et al. (2002) and Wei et al. (2001), but these methods are not robust enough to the different environments. Edge features of the car image are very important, and edge density can be used to successfully detect a number plate location due to the characteristics of the number plate. Ming et al. (1996) developed a method to improve the edge image by eliminating the highest and lowest portions of the edge density to simplify the whole image. But some of the plate region identity will be lost in this method.
车牌识别英文文献1翻译
提出的模型本文的主要目的是要开发一个系统可以从没有虚假质量的复杂的现场图像中提取车牌号码，相机和车牌之间的距离，其中的相对车牌已被抓获在相机等。
对车牌识别系统概述见图1，在车辆图片由相机拍摄后，它会被传递到预先处理单位由系统作进一步处理。
其主要功能是消除图像采集子系统所造成的噪声，提升图像的其他两个子系统使用的功效。
图像将被板提取模块扫描以找到车辆的车牌。
下一阶段是对于车牌中字符的分割。
最后每个字符将传递给光学字符识别（OCR）模块来进行识别确定，最终结果将是ASCII字符和车牌号码。
3.1.预处理输入图像的最初处理是为了提高其质量，并为系统的下一阶段执行作准备。
首先，该系统使用的NTSC标准的方法将RGB图像转换为灰度图像。
G=0.299*R+0.587*G+0.114*B第二步，用中值滤波（5x5）对灰度图像进行处理，以消除噪音，同时也能保持图像的清晰度。
中值滤波是一种非线性滤波器，它用各像素5x5邻里的计算得出中位值来取代该像素的值。
3.2.板块提取板块提取处理包含五个不同的阶段进行，如图2所示；在这里每个阶段执行灰度图像分割过程以消除不属于车牌区域的多余的像素。
例如，水平定位阶段是负责寻找水平部分可能包含一个车牌。
在下面的讨论中，每一个阶段都会被细细展开讨论。
马来西亚车牌由一排白色字符在黑色的背景底色，所以我们可以说，车牌区域的特点是从一排黑色过渡到一排白色，反之亦然，这样转换被称为“边缘”。
从车牌字符到其背景在色彩强度上总的变化叫做边缘的强度。
最强边缘值，能够在从一个黑色像素过渡到一个白色像素或从白色像素变为黑色像素情况下被找到。
在理想的情况下，马来西亚的车牌是白色的字符绘制在黑色的背景上，因此这种搭配产生了高强度边缘值，用于查找出可能的板区域。
在本文中我们将使用Sobel算子来查找边缘。
Sobel运算在图片上执行一个2-D空间梯度测量。
通常它是用来寻找近似绝对的梯度幅度对在每一个点在输入的灰度图像上。
车辆图像预处理和车牌定位的方法研究 中英文对照翻译
The Method Research of Vehicle Image Preprocessing and LicensePlate LocationAbstract—Aiming at the characteristics of vehicle images, this paper presents a method about vehicle image preprocessing and license plate location. The image preprocessing mainly includes graying the image, detecting the edge on image, median filtering and taking binaryzationon image. The license plate location consists of locating upper and lower boundary, locating left and right boundary for the image after preprocessing and finally the license plate region located. All the above is the foundation for the subsequent license plate recognition. A large number of experiments have proved that this method has the good image preprocessing effect, high accuracy rate, location speed and the good practical value.Keywords-Preprocessing; Median filtering; Binaryzation; Edge detection; License plate locationI. I NTRODUCTIONVehicle license plate recognition system based on vehicle license for the specific target is dedicated computer vision system[1]. It is one of the important research topics about computer vision and pattern recognition technology in the field of intelligent transportation applications. Vehicle license identification is the general composed by the following process: image acquisition, image preprocessing, license plate location, character segmentation, character recognition[2]. The correct rate of the last process has a direct impact on the next process. Since the original image from the acquisition card includes the vehicle license plate, the car itself, and automotive background image, it is necessary to remove these non-licensed images in order to extract the correct regional license and for the foundation of the license plate character recognition . In the actual system, due to natural changes in day and night illumination, vehicle own movement, the camera angle of observation, collecting images of the equipment itself and other factors influence, the image obtained is not always very satisfactory, there is a wide range of noise. Therefore, it is necessary to make the license plate image pre-processing for improving image quality, layingfoundation for the subsequent license plate recognition.II. I MAGE PREPROCESSINGImage preprocessing is an essential process in license plate recognition system, and the quality of preprocessing directly affects the location. The image preprocessing in this article includes image grey, edge detection, median filter andbinaryzation. The following gives the detail statement on the preprocessing process.A. Image greyingAll vehicle images acquired through camera and image card are color image, and image format is not same. The commonly used image forms are JPEG and BMP. If treatment with acquired image directly, not only the image format is complex, moreover the computation data quantity is extremely huge, such license plate location cannot satisfy the request for fast and real-time. Therefore, the color image need to be formatted processing, transforming the JPEG image or the BMP image to DIB (Device Independent Bitmap) which favors the computer to process. Then, using R, G and B tricolor weighted average method process the DIB image, processing function is shown as equation (1):F(x, y) =0.299*R(x, y) +0.587*G(x, y) + 0.114*B(x, y) (1)R(x, y), G(x, y) and B(x, y) are R, G and B tricolor component of the input color image [3]. The color image transforms to grey image by equation (1) processing, the result is shown in Fig. 1.(a) Original image(b) Grey imageFigure 1. A contrast between original image and grey imageB. Edge detectionEdge is the most basic feature of the image, so the edge indicates step change of the grey level on its surrounding pixels. Chinese vehicles license plate region has big color contrast between license plate bottom and license plate character. The license plate is composed of 7 characters with rich edge information in turn which are the Chinese character, the letter and the Arabic numeral, and the character in license plate region and background have obvious edge in the entire picture, also have many edge. This is one of the basic characteristics that license plate region distinguishes from other region in the vehicles picture, and it is also the fundamental basis of this algorithm. Commonly used edge detection operators have Prewitt operator, Sobel operator, Canny operator, LOG operator, Roberts operator and other operator. Prewitt operator and Sobel operator are first-order differential operator, the former is the average filter, the latter is the weighted average filter ，while the image edge detected by the two methods may be better than two pixels. The Canny method uses first derivative as the foundation to judge edge points. It is one of the best traditional first-order differential operators in the detection of step edge. The shortcoming is smoothing out some details [4]. LOG operator uses Gaussian function to smooth image first, then uses Laplace transform to process image, and this method processing image edge is insufficiently clear and the speed needs to be improved. The localization using Roberts operator is quite precise, but more sensitive to noise. The experiment indicated that using the Prewitt edge detection operator can better stand out the edge characteristic of license plate, and speed is faster. Fig. 2 is several imagesafter process by different edge detection operator.(a) Prewitt operator(b) Sobel operator(c) Canny operator(d) LOG operator(e) Robert operatorFigure 2.Image contrast after process by several edge detection operator C. Median filteringMedian filtering method is a non-linear smoothing technique. It sets the grey level of each pixel to the middle value of all pixels’ grey level in a neighborhood window [5]. Median filtering method is a non-linear technique that based on a sequencing statistic theory. It can inhibit the noise effectively. The basic principle of median filtering is to replace the value of point in digital image or numerical sequences with the middle value of this point’s one neighborhood, so it can let the around pixel value close to this point’s value. Thus the isolated noise is eliminated. This method utilizes the two-dimensional sliding template of a certain structure, arranges the pixel in template according to the size of pixel value, then a rise (or drop) two-dimensional data array was produced. The output of two-dimensional median filtering result provided by equation (2):G(x, y) =med {F(x-k, y-l)} (2)F(x, y), G(x, y) is respectively for original image and the image after dealing with. W is a two-dimensional template. The result is shown in Fig. 3.Figure 3. Median filter imageD. Image binaryzationImage binaryzation processing is that setting gray value of pixels on image to 0 or 255, that is, the entire image presents tangible black and white effect [6]. We obtain the binaryzation image through selecting 256 brightness level of grey image by suitable threshold. Binaryzation image can still reflect the whole and partial characteristic of image. In digital image processing, binaryzation image holds the extremely important status. First, binaryzation reduces the amount of image data. Secondly, to highlight the outline of the interest goal, this is favor to further processing. Fig. 4 is the image after binaryzation processing.Figure 4. Image after binaryzation processingIII. LICENSE PLATE LOCATIONThe task of license plate location is to remove most unwanted background information from the whole image and find the license plate region with a small amount of redundant background. Because the license plate region contrasts to the background, the histogram of license plate image shows a bimodal shape after image preprocessing. The wave trough between two wave peaks corresponding to the gray level is selected as a threshold. Supposing the image is divided by F(x, y) and the gray level range is [Z1, Zk]. Fig. 5 shows there are two obvious wave peaks in gray levelZi and Zj, and in Zt there is a wave trough. By choosing Zt reasonably, B1 belt can contain grey level correlation to the background as far as possible, while the B2 band includes grey level correlation to the license plate as far as possible [7].Figure 5. Double peak of histogramA. Locating upper and lower boundaryOne characteristic of license plate image is the crowded characters in the internal, so the grey jump is extremely fierce. We find the possible location of license plate region by using row grey jump rule of grey image. We preserve this position and call it as the fake license plate region. Specific algorithm including following steps:Step1：Calculating the level histogram of image, and smoothing the level histogram with [1,1,1,1,1] / 5 operator.Step2：Searching the bottom edge distance of license plate from the image base, if 5 line which is predefined as 5 has satisfied the request continuously which the value is bigger than or equal to 10 pixels in histogram, and the value of current line differs above 4 pixels with the value of front the Nth line, then we believe that the bottom edge distance of license plate has founded. Current line minus 5, and locates the scan line to the summit of current peak. If the current line does not satisfy the condition, then continues to search upwardly until the top margin of image.Step3：Locating the current line to the bottom of up wave crest, if the peak bottom value is greater than the maximum value, then locating to the summit of current peak, and the summit for maximum value line; searching upwardly from the current line’s next line, if the value of search line is greater than the recorded maximum value, then setting the current line as maximum value and carrying on searching upwardly from it. Otherwise, if the current value is smaller than two-thirds of maximum value, or the current value is less than 5 pixels, or the license plate’s height is greater than 80 pixels,then we think the top margin of license plate has been founded.Step4：Check whether the license plate’s height complies with the requirement or not. If the license plate’s height is smaller than 40 pixels to continue search upwardly, otherwise the license plate region has been found, and precision positioning is from up and down location of license plate.After above 4 steps searching, the upper and lower boundary of license plate has been found. Location result is shown as in Fig. 6.Fig. 6. Locating upper and lower boundaryB. Locating left and right boundaryWe can find the left and right boundary by the rule of character change in license plate. Specific algorithm including following steps:Step1：To the vertical histogram, scanning from left to right, the points less than 4 pixels in the histogram are removed firstly.Step2：The current line is written for the nLeft, as the beginning of the peak.Step3：Adding the rows that greater than or equal to 4 pixels in the cumulative histogram up, and recording it as the width of peak: Pixel1Wide.Step4：Adding the rows that smaller than or equal to 4 pixels in the cumulative histogram up, and recording it as the width of trough: Pixel0Wide. If the width of current peak is less than 4 and the average peak height is less than one-sixth of height, and the supreme value is less than a quarter of height, then merging this peak into the trough of upper peak, and to Step 1.Step5：WaveCrestCount adds 1. WaveCrestCount is the number of peak.Step6：Repeating Step 1 until the current row number is greater than the image width. Step7：Statistic all peaks, when seven consecutive width of wave trough is smaller than the height of license plate, the wave peak of left side is regarded as the left edge distance of license plate.Step8：Counting backward continually, until meeting a width of wave trough is greater than the height of license plate. The start point of wave trough is regarded as the rightedge distance of the license plate.The result of locating left and right boundary is shown as in Fig. 7.Fig. 7. Locating left and right boundaryIV. CONCLUSIONSThis paper mainly researches the preprocessing of license plate image and license plate location. The preprocessing not only removes the noise in the image but also processes edge detection to the license plate image. After preprocessing, according to the characteristic of license plate image and the regularity of grey change，the boundary of license plate is located. The use of the methods are proposed in this article, in a variety of weather conditions and under the conditions of different backgrounds 200 license plate images are collected and implemented the automatic positioning of the plate. The method can be more rapid and effective to identify the license plate from the complex background noise. Its feature detection has good anti-interference effect, can meet the real-time system's demands and has good application prospects.车辆图像预处理和车牌定位的方法研究摘要—针对车辆图像的特征，本文提出了一种车辆图像预处理和车牌定位的方法。
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。
有效的车牌定位方法
车牌定位的一个有效方法Danian Zheng *, Yannan Zhao, Jiaxin Wang国家重点实验室智能技术与系统、计算机科学与技术系、清华大学、北京100084、中国摘要车牌定位是机动车牌照自动识别运输系统的一个重要阶段。
本文提供了一个实时和强劲车牌定位方法。
车牌区域包含丰富的边缘和纹理信息。
我们先用图像增强和Sobel算子提取出图象的垂直边缘, 然后用一个有效算法除去图像的大部分背景和噪声边缘, 最后在剩余边缘图像中利用一个矩形窗口搜索车牌区域并从原始图像中将车牌切割出来。
实验结果表明,我们的方法有很强的鲁棒性和很高的效率。
_ 2005 Elsevier B.V. All rights reserved。
关键词:图像增强; 边缘检测; 长曲线和随机噪声的去除; 车牌定位和分割1.引言车牌识别成为当今许多自动交通管理系统如公路交通管理, 公路自动缴费和桥梁或停车场出入管制的关键技术。
车牌定位是这项技术中非常必要和重要的一个阶段,它已引起了相当大的关注。
研究人员已经找到许多不同的方法定位车牌。
Rodolfo 和Stefano(2000)制定了一种基于矢量量化(VQ)的方法。
矢量图像是基于一种特定的编码机制的四叉树，它可以提供给系统关于图像区域所包含内容的一些线索，这些信息有助于定位的实现。
Park et al. (1999)使用神经网络定位车牌。
神经网络可作为过滤分析图像的小窗口,判断每个窗口是否包含车牌,其输入为HIS值。
一个后处理器将这些过滤图像合并起来并在图像中定位跳跃的车牌区域。
除去神经网络,其他过滤方法也被研究过。
例如,有些作者用线敏感过滤器提取车牌区域。
可以确定车牌区域由很高密度的薄暗线或曲线。
因此,车牌的定位是一个在图像中寻找包含由一个累积函数计算能得到的最大线过滤值的矩形区域的操作(Luis et al. 1999)。
车牌字符可以直接通过对输入图像的扫描和寻找到图像中彼此不相连的部分来识别。
车牌定位方法综述
收稿日期:２０１８ － ０５ － １５ 基金项目:攀枝花学院大学生创新创业训练计划项目(２０１７ｃｘｃｙ０８１) 作者简介:罗 山(１９７９￣ ) ꎬ男ꎬ四川乐至人ꎬ讲师ꎬ硕士ꎬ主要研究方向:图像处理与模式识别在智能交通中
１) 基于脉冲神经网络的方法ꎮ 利用脉冲神经 网络对车牌图像进行颜色特征提取ꎬ实现车牌的粗 定位ꎬ再对粗定位车牌进行预处理ꎬ采用行列扫描投
影法进 行 精 确 定 位ꎬ 最 终 提 取 出 正 确 的 车 牌 区 域[２] ꎮ
２) 基于级联卷积神经网络的方法ꎮ 主要是针 对多车辆、低分辨率等复杂环境下的车牌定位情况ꎬ 通过运动目标检测算法定位出目标运动热点区域ꎻ 然后使用卷积神经网络识别热点区域中的车辆ꎻ最 后使用卷积神经网络从定位的车辆图片中识别车 牌[３] ꎮ １. ３ 基于形状回归的方法
主要是通过局部图像增强处理改善图像质量变 化较大的问题ꎬ从而获得理想的车牌特征描述ꎬ结合 量子粒子群优化算法快速、高效的特点在全图范围 选择最满足车牌特征的区域位置[８] ꎮ １. ８ 基于颜色和边缘信息的方法
利用 ＲＧＢ 颜色空间提取符合车牌颜色的区域ꎬ 再通过边缘检测提取车牌边缘信息ꎬ根据车牌的颜 色信息和边缘信息融合后进行形态学提取车牌候选 区域ꎬ然后使用车牌规整度计算[９] 进行车牌区域的 筛选ꎬ从而定位车牌区域ꎮ １. ９ 基于 Ａｄａｂｏｏｓｔ 算法的方法
山西电子技术 ２０１９ 年第 １ 期
综 述
文章编号:１６７４ ￣ ４５７８ ( ２０１９ ) ０１ ￣ ００９４ ￣ ０３
车牌定位方法综述∗
罗 山ꎬ 李玉莲
车辆识别相关中英文翻译
伊朗车牌识别使用连接组件和聚类技术H.R.艾因Moghassemi 伊斯兰阿萨德大学（西德黑兰）伊朗德黑兰摘要车牌识别系统（LPR），在许多应用发挥了重要作用，如访问控制，流量控制，被盗车辆的检测。
一个车牌识别系统可分为检测和识别阶段。
对于车牌检测，有一些相关的建议和方法，就是对于水平板块和垂直板块的检测。
车牌的准确的定位是认识到连接的成分分析和聚类技术研究。
由于对车辆定位的是摄像头，车牌矩形可以旋转所以在许多方面都会产生倾斜。
因此倾斜检测和校正车牌就很有必要。
在这项研究中一个有效的歪斜检测和识别方法是泽尼克旋转和尺度小波矩特征不变法用于车牌字符识别。
和以上不同的是演算法是在强光照条件下，视角，位置，大小和颜色在复杂的环境中运行时处理车牌。
“成功是在各种条件整体性能达到车牌用于车牌识别系统时的93.54％。
关键词：车牌识别;倾斜检测;斜线改正;泽尼克和小波矩;旋转和尺度不变。
一，导言LPR（车牌识别）是一种用于识别车辆牌照的图像处理技术。
LPR系统中使用于各种安全和交通应用，例如在图1。
LPR系统是用来在网关的访问控制。
在图1：当车辆到达大门时，自动车牌识别系统“读”车牌字符，与预定义的列表比较，如果有一个匹配则打开大门。
“LPR系统是在1976年首次由英国的分公司在警察科学开发。
原型LPR系统是在1979年工作。
自1994年以来，伊朗第一个研究LPR系统的工作，开始在伊朗大学科技和技术（IUST）和控制交通总公司开始实行[1]。
车牌识别使用图像处理软件分析图像捕捉车辆和定位提取车牌; 然后用光学字符识别（OCR）系统对车辆图像进行车牌字符识别。
他们还利用在各种警察,军队和使用电子收费payper道路和交通的分类活动或个人。
LPR可以用来存储由相机拍摄的图像以及一些车牌字符和数字，并且存储驱动程序。
LPR系统使用红外照明或图像加工技术，让相机拍摄照片处理。
车牌识别系统的软件部分运行于中央，可以连接电脑和其他应用程序或数据库。
详解车牌识别技术之车牌定位
详解车牌识别技术之车牌定位车牌识别系统(Vehicle License Plate Recognition，VLPR) 是计算机视频图像识别技术在车辆牌照识别中的一种应用，能够将运动中的车辆牌照信息（含汉字字符、英文字母、阿拉伯数字及号牌颜色）从复杂背景中提取并识别出来，通过车牌提取、图像预处理、特征提取、车牌字符识别等技术，识别车辆牌号、颜色等信息，目前最新的技术水平为字母和数字的识别率均可达到99%以上。
车牌识别是现代智能交通系统中的重要组成部分之一，应用十分广泛。
它以数字图像处理、模式识别、计算机视觉等技术为基础，对摄像机所拍摄的车辆图像或者视频序列进行分析，得到每一辆汽车唯一的车牌号码，从而完成识别过程。
当前，车牌识别技术已经广泛应用于停车管理、称重系统、静态交通车辆管理、公路治超、公路稽查、车辆调度、车辆检测等各种场合，对于维护交通安全和城市治安，防止交通堵塞，实现交通自动化管理有着现实的意义。
不过，你知道车牌识别技术是如何实现车牌定位的吗？车牌定位，就是在车牌图像中找出最符合车牌特征的区域。
其主要目的是在经图像预处理后原始灰度图像中寻出车牌的位置，并将包含车牌字符的一块子图像从整个图像中分割出来，供字符识别子系统之用，分割的准确与否直接关系到整个车牌字符识别系统的识别率。
车牌识别系统现阶段比较成熟的车牌定位方法有：基于图像的彩色信息法、基于纹理分析的方法、基于边缘检测的方法、基于数学形态学的方法、基于遗传算法的定位、基于神经网络定位等。
汽车牌照定位：在车牌识别系统中对车牌定位的算法包括三个过程，即颜色识别、形状识别、纹理识别。
先通过颜色识别来初步确定车牌的所在区域，再结合车牌的形状特征以及纹理特征精确定位。
车牌识别系统都是基于牌照区域的特征来进行定位的，车辆牌照的主要特征如下：1、颜色特征车牌底色与字符颜色有着相应的组合，颜色对比强烈。
如果对彩色图像进行定位，有蓝底白字白框线，黄底黑字黑框线，黑底白字白框线，白底黑f红1字黑框线等几种颜色搭配的车牌。
车牌定位技术研究
车牌定位技术研究李艳;王学军【摘要】车牌识别是智能交通系统的重点研究方向之一,车牌定位是车牌识别的重要技术环节.笔者研究了基于canny算子的边缘检测和数学形态学运算的车牌定位算法,并对算法进行分析验证.结果表明该车牌定位方法准确率较高,有利于车牌识别.【期刊名称】《河北省科学院学报》【年(卷),期】2013(030)002【总页数】5页(P35-39)【关键词】车牌识别;车牌定位;形态学【作者】李艳;王学军【作者单位】石家庄铁道大学信息科学与技术学院,河北石家庄050043;石家庄铁道大学信息科学与技术学院,河北石家庄050043【正文语种】中文【中图分类】TH133;TP183汽车交通是现代社会的重要标志之一，车辆数量的急剧增加导致交通阻塞日益严重、交通事故频发、城市交通压力增大，给人们日常生活带来严重影响。
传统的交通机制不能满足人们需求，智能交通系统ITS（Intelligent Transportation System）应运而生。
其中，车牌识别系统LPR（License Plate Recognition）是计算机视觉、图像处理以及模式识别技术在智能交通领域应用的重要研究方向之一，是实现交通管理智能化的重要环节。
车牌识别系统的关键环节主要有车牌定位与提取、车牌倾斜校与字符分割以及字符识别［1］。
在此将重点讨论车牌定位算法1 车牌的定位从图1车牌识别系统流程可见，只有准确地定位出车牌的位置，才能进行车牌校正、分割、识别，最终得到准确的车牌信息。
车牌的定位指的是从摄取的图像中找到汽车牌照的位置，并把含有车牌的子区域取出来，是车牌识别系统的关键技术之一。
目前常用的车牌定位技术主要有：彩色图像法、纹理特征分析法、灰度边缘检测法［2］。
图1 车牌识别系统流程彩色图像法通过利用图像的颜色信息进行车牌定位，当车牌和车身的颜色相似时，不能够准确定位。
纹理特征分析法能够很好地应对车牌倾斜，光照不均或偏弱偏强等影响，但对噪声太过敏感，只适用于外部噪声较少的情况。
车牌识别外文翻译文献综述
车牌识别外文翻译文献综述(文档含中英文对照即英文原文和中文翻译)License Plate Recognition Based On Prior KnowledgeAbstract - 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, vehicle license plates, neural network.I. INTRODUCTIONVehicle 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 FEATURES 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 belongsto 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 Chinese provinces. 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 flowchartof 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.Fig.2 The flowchart of LPR systemA. 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 of 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:(1)Characters edge detection Binary image segmentingCandidate image detectionVehicle plate extractionImage acquisitionPlate locationCharacters segmentationclassifierChinesecharacter Letter Letter or number Number Special characterCharacters recognition(2)Here, the Gaussian variance is set to be less than W/3 (W is the character stroke width), P gets its maximum value M at the center of the stroke. After convolution, binarization is so1performed 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.Fig. 4 The whole process of locating license plate2) 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.Fig. 5 The flowchart of the character segmentationFirstly, 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.TABLEIIEnd of algorithmwhere 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 S is the initial coordinates for the character segment, and 2i S is the final coordinate for the character segment. The distance 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 gradient 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.Fig. 7 The network topologyAs 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 output vectors 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.Fig. 8 The architecture of a neural network for character recognitionAs Fig. 8 shows, firstly the classifier decides the class of the input feature 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 Input character vectorfor recognition Neural network output Input character vectorfor recognition Neural network Target outputerrorcharacterized by the recognition rate which is defined by equation (5).Recognition rate =(number of correctly read characters)/ (number of found characters) (5)TABLE IIIIV. COMPARISON OF THE RECOGNITION RATE WITH OTHER METHODS In 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 the method 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.TABLE IVTABLE VV. 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 ChineseVLP 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.REFERENCES[1] P. Davies, N. Emmottand N. Ayland,“License Plate Recognition Technology for Toll Violation Enforcement”Proceedings of IEE Colloquium on Image analysis for Transport Applications, Vol. 035, pp.7/1-7/5, February16, 1990.[2] V. Koval,V. Turchenko,V. Kochan, A. Sachenko, G. Markowsky,“Smart. License Plate Recognition System Based on Image Processing Using Neural Network”IEEE International Workshop on Intelligent Data Acquisition and Advanced Computing System: Technology Applications8-10September2003.[3] Abdullah, S.N.H.S.; Khalid, M.；Yusof, R.; Omar, K.“License Plate Recognition using multi-cluster and Multilayer Neural Networks”Information and Communication Technologies, 2006. ICTTA '06. 2nd Volume1, 24-28April2006Page(s):1818–1823.[4] Nathan, V.S.L.; Ramkumar, J.; Kamakshi Priya, S.“New approaches for license plate recognition system”Intelligent Sensing and Information Processing, 2004. Proceedings of International Conference on2004Page(s):149–152.[5] Mei Yu; Yong Deak Kim,“An approach to Korean license plate recognition based on vertical edge matching”Volume4, 8-11Oct. 2000Page(s):2975 - 2980vol.4. [6] Tindall, D.W.”Application of neural network techniques to automatic licence plate recognition”Security and Detection, 1995., European Convention on16-18May1995Page(s):81–85.[7] Aghdasi, F.; Ndungo, H. “Automatic licence plate recognition system”AFRICON, 2004. 7th AFRICON Conference in AfricaVolume1, 2004Page(s):45 - 50Vol.1[8] Richard O.Duda Peter E.Hart David G.Stork,“Pattern Classification Second Edition”PP 333–373.[9] Standard for vehicle license plate number in the People's Republic of China (GA36-92).[10] Richard O.Duda Peter E.Hart David G.Stork,“Pattern Classification Second Edition”PP373–376.[11] Nukano, T.; Fukumi, M.；Khalid, M.;“Vehicle license plate character recognition by neural networks”Intelligent Signal Processing and Communication Systems, 2004. ISPACS 2004. Proceedings of2004International Symposium on18-19Nov. 2004Page(s):771–775.[12] Xiaojun Chi; Junyu Dong;Aihua Liu; Huiyu Zhou,“A Simple Method for Chinese License Plate Recognition Based on Support Vector Machine”Communications, Circuits and Systems Proceedings, 2006International Conference on Volume3, June2006Page(s):2141 - 2145.[13] Yo-Ping Huang；Shi-Yong Lai；Wei-Po Chuang, A template-based model for license plate recognition”Networking, Sensing and Control, 2004IEEE International Conference on Volume2, 2004Page(s):737 - 742Vol.2.译文:基于先验知识的车牌识别摘要-本文基于一种新的改进的BP（反向传播）神经网络算法对中国的车辆车牌识别(LPR)进行了介绍。
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ABSTRACT
Automatic License plate recognition system is an important research subject in intelligent traffic，which mainly includes three key components: license plate location, license plate character segmentation and license plate character recognition. This thesis makes an intensive study on license plate location, which is a key part of the license plate recognition system. The purpose of this thesis is to find a simple and practical way to performing the function of license plate location, based on image segmentation algorithm and the application of MATLAB simulation.
There are a variety of methods of license plate location at present. On the basis of the analysis and comparison of the characteristics of various algorithm, quick and simple realization of license plate location can be achieved by the application of the combination of image segmentation, mathematical morphology and projection operation. This algorithm mainly includes two aspects, image preprocessing and license plate localization.In the image preprocessing parts, it carries out image gray-scale processing, edge detection and binary processing to highlight image edges information at first，and then making the vertical direction structure elements corrosion combined with a closing operation to fill the tiny holes in the license plate area to make it become a connected area. In the license plate location parts, by scanning image from rows and columns respectively，counting up the grayscale pixels，combining with horizontal projection and vertical projection，license plate location can be achieved with the utilization of characteristics of license plate location and size. The method has been proved to be effective for accurate positioning the license plate location.
Key words：Image Segmentation; License Plate Location; Mathematical Morphology;
Projection
摘要
车牌识别系统是智能交通中一个重要的研究课题，主要包括车牌区域定位、车牌字符分割、车牌字符识别三个关键部分。
本文主要针对车牌自动识别系统中关键部分之一的车牌区域定位方法做了深入研究，基于图像分割算法，通过应用MATLAB仿真寻找一种简单实用的方法完成车牌定位。
目前车牌定位方法有很多，本文在分析比较各种算法特点的基础上，应用图像分割、数学形态学和投影运算相结合的方法，快速简单地实现车牌定位。
该算法主要包括图像预处理和车牌定位两个方面。
在图像预处理部分，首先对图像进行灰度转换、边缘检测和二值化处理，以凸显图像的边缘信息；然后用垂直方向的结构元素进行腐蚀，结合一次闭运算来填补车牌区域内细小孔洞，使其成为一个连通区域。
在车牌定位部分，分别对图像做行和列扫描，统计灰度像素点，通过水平投影和垂直投影结合，利用车牌位置和大小特性，实现车牌区域定位。
实践证明该方法对于车牌的准确定位是有效的。
关键词：图像分割；车牌定位；数学形态学；投影法。