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)

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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。

车牌识别系统毕业论文

车牌识别系统毕业论文论文(设计)题目车牌识别系统——车辆牌照定位系统的设计与实现院系名称计算机科学与技术系专业（班级）计算机科学与技术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AbstractAs 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目录摘要 (I)Abstract ................................................................................................................................................................ I I 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 (2)1.3.2 基本思路 (2)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 (4)2.4.1 图像二值化 (4)2.4.2 边缘检测 (4)2.4.3 图像增强 (5)第三章车牌定位方法研究 (6)3.1 车牌定位常用方法介绍 (6)3.1.1 基于纹理特征分析的定位方法 (6)3.1.2 基于数学形态学的定位方法 (6)3.1.3 基于边缘检测的定位方法 (6)3.1.4 基于小波分析的定位方法 (6)3.1.5 基于图像彩色信息的定位方法 (6)3.2 基于行扫描灰度跳变分析的车牌定位方法 (7)第四章车牌定位系统的设计与实现 (8)4.1 车牌定位系统系统分析 (8)4.1.1系统业务需求 (8)4.1.2系统用户需求 (8)4.1.3系统功能需求 (8)4.1.4 系统运行环境需求 (8)4.2 车牌定位系统的整体架构设计 (8)4.2.1 系统总体架构 (8)4.2.2 系统技术架构 (9)4.3 车牌定位系统的功能模块划分和实现 (10)4.3.1 系统的功能模块划分 (10)4.3.2 系统的功能模块实现 (11)第五章车牌定位系统的系统测试 (16)5.1 系统测试过程 (16)5.2 系统测试结果 (17)5.3 测试结果分析 (24)第六章技术要点回顾 (26)6.1 难度分析 (26)6.2 主要工作 (26)6.3 应用的主要技术手段 (26)6.4 存在的问题及展望 (27)结论 (28)参考文献 (29)致谢 (30)第一章绪论1.1 课题的来源及意义随着全球各国汽车数量的持续增加，城市的交通状况越来越受到人们的重视。

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。

实时的车牌识别系统中英文

VISL 项目在完成了02年一种实时车牌识别（LPR）的系统由酒吧，母鸡罗恩指导单位约哈难埃雷兹该系统一个典型的模式：摘要这个项目的目的是建立从汽车板在门入口处时，例如A区牌照时停车一个真正的应用程序，它已承认。

该系统具有视频摄像机的普通PC机，渔获量的视频帧，其中包括一个明显的汽车牌照和处理它们。

一旦发现车牌，它的数字确认，并显示在用户界面或数据库核对一。

形象的重点是设计一个单一的算法车牌从用于提取，分离板的特点及识别单个字符。

背景：目前已在实验室过去类似的项目。

包括项目实施的整个系统。

这个项目的目的首先是改善方案的准确度，并尽可能其时间复杂度。

该实验室的所有项目在过去。

根据精度不佳的测试中，我们就程序设置的45个影像，我们用我们的成功，并只有在特定的条件感到满意。

出于这个原因，除了再次从非常罕见的情况下，整个程序写。

简要说明执行情况：我们的车牌识别系统可大致分为以下框图。

框图全球系统。

另外这个进程可以被看作是减少或地方的牌照抑制有害信息从携带信息的信号，这里是一个视频序列包含大量无关信息的特点，形式抽象符号的研究。

光学字符识别（OCR）已采用神经网络技术，采用神经元在输出层的前馈网络的3层，200个神经元在20输入层，中间神经元在10层，。

我们保留了神经网络数据集图像用在项目的先例，其中包括238位第我们的算法的详细步骤说明如下图：框图程序的子系统。

这里介绍捕获帧的一个给定的产出上面所述的主要步骤：示例捕获帧黄色区域捕获的帧过滤捕获帧地区扩张黄色车牌区域确定氡角度的变换板的使用改进的LP地区调整唱片轮廓-列和图调整唱片轮廓-线条和图唱片作物灰度唱片唱片二值化，均衡使用自适应阈值二进制唱片归唱片确定使用的LP水平轮廓图像总和先决行归唱片轮廓调节字符分割使用的山峰到山谷方法扩张型数位影像调整数字图像水平轮廓-线和图调整的数字图像轮廓调整大小的数字图像OCR的数字识别的神经网络方法工具该方案实施开发了基于Matlab。

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。

据我所知，除文中已经注明引用的内容外，本论文（设计）不包含其他个人已经发表或撰写过的研究成果。

对本论文（设计）的研究做出重要贡献的个人和集体，均已在文中作了明确说明并表示谢意。

作者签名：日期：毕业论文（设计）授权使用说明本论文（设计）作者完全了解**学院有关保留、使用毕业论文（设计）的规定，学校有权保留论文（设计）并向相关部门送交论文（设计）的电子版和纸质版。

有权将论文（设计）用于非赢利目的的少量复制并允许论文（设计）进入学校图书馆被查阅。

学校可以公布论文（设计）的全部或部分内容。

保密的论文（设计）在解密后适用本规定。

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。

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。

车牌识别系统中车牌定位及分割技术研究

车牌识别系统中车牌定位及分割技术研究摘要随着经济社会的进展，我国汽车数量，尤其是私家车数量大量增加。

这对交通公共基础设施的建设和与其配套的车辆管理系统提出了更高的要求。

为实现道路交通管理的自动化和车辆行驶的智能化，各类智能交通系统应运而生。

汽车牌照是肯定汽车的有效手腕，因此车辆牌照识别技术在智能交通管理中发挥着基础性的重要作用。

车牌识别技术主要包括以下三个部份:车牌定位技术、车牌的字符分割技术和字符识别技术。

车牌定位的任务就是肯定出车牌在图像中的具体位置;车牌定位是车牌识别系统完成图像收集后对图像进行处置的第一步，分割是对车牌进行识别的基础。

本文的对车牌识别系统中车牌定位及分割研究的大致方式为：第一对图像进行预处置，包括灰度化、维纳滤波去噪和利用边缘检测函数对图像边缘化，再利用Hough变换对图像进行矫正，按照边缘图像的直方图对图像进行切割，提取主要车牌区域。

最后按照主要车牌区域的灰度直方图肯定二值化阈值，按照车牌区域特征对主要车牌区域进行字符图像有效信息的分割。

用MATLAB软件对上述步骤进行仿真，实验结果表明，应用上述方式能够分割出汽车牌照图像的有效信息，而且效果较好。

关键词：图像分割；边缘检测；车牌识别Research on License Plate Localization and Segmentation TechnologyAbstractWith the development of economic society, number of cars in our country, especially the number of private cars has increased a lot。

This have put forward higher requirements for the traffic infrastructure construction and its supporting vehiclemanagement system. In order to realize the road traffic management automation and the vehicle intelligent, intelligent traffic system emerge as the times require. Vehicle license plate is a effectively way to identify the car, thus the vehicle license plate recognition technology plays a fundamental role in intelligent traffic management. License plate recognition technology mainly include three parts，such as the license plate location technology, the character segmentation technology and the character recognition technology. The task of license plate location technology is to determine the specific location in image license. License plate location is the first step. The character segmentation technology is based on license plate recognition. In this paper, the method about license plate recognition system for license plate location and segmentation is, firstly, image preprocessing, including gray-scale, Wiener filter denoise and edge detection function of image edge. Second using Hough transform for image correction, cutting the image according to the histogram of edge of image to extraction of main plate region. Finally, according to the histogram of the gray image of the main plate region to determine the threshold of binarization and according to the characteristics of license plate region to segmentation the effective information of the character image. The experimental results show that, using MATLAB software simulation, application of the method can segment the effective information of the character image in license plate, and the effect is better.Key words: Image segmentation; Edge detection; Vehicle license plate recognition目录摘要 (1)Abstract (2)目录 (4)1 绪论 (5)选题的背景和目的 (5)国内外研究状况 (6)应用范围 (7)本论文内容介绍 (8)2............................................................................................................................................. 车牌图像预处置9车牌区域特征 (9)图像的灰度化 (10)图像的去噪 (12)2.3.1线性低通滤波 (12)2.3.2维纳（Wiener）滤波 (13)边缘检测 (14)2.4.1 Roberts算子 (14)2.4.2 Sobel 算子 (15)2.4.3 P rewitt算子 (15)2.4.4 LOG算子 (16)2.4.5 Canny算子 (17)2.4.6拉普拉斯算子 (17)3倾斜度矫正与车牌区域定位 (20)倾斜度矫正 (20)图像的定位 (26)4 二值化与字符分割 (28)图像的二值化 (28)字符的分割 (31)4.2.1垂直投影分割 (31)4.2.2字符结构特征分割 (33)结论 (36)致谢 (37)参考文献 (38)附录A设计主程序 (39)1 绪论选题的背景和目的随着经济全世界化和信息时期的来临，运算机技术、通信技术和运算机网络技术进展超级迅速，自动化的信息处置能力和水平也在不断提高，并在人们的社会活动和实际生活的各个领域取得普遍应用。

车牌定位技术研究毕业设计论文含开题报告

本科毕业设计（论文）题目：车牌定位技术研究Graduation Design (Thesis)Research on License Plate Location TechnologyByLU PengSupervised byAssociate Professor LUO Shao XinAssistant Professor HAO Teng FeiNanjing Institute of TechnologyJune, 20151本科毕业设计（论文）开题报告题目: 车牌定位技术研究毕业设计（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。

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本人完全意识到本声明的法律后果由本人承担。

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文献翻译+原文

一种马来西亚车牌定位识别系统Velappa GANAPATHY 1School of Engineering, Monash University Malaysia 2 Jalan Kolej, Bandar Sunway, PetalingJaya, Selangor MalaysiaandWen Lik Dennis LUI 2School of Engineering, Monash University Malaysia2 Jalan Kolej, Bandar Sunway, Petaling Jaya, Selangor Malaysia摘要：在交通系统中，智能科技产品广受欢迎。

这些只能系统给不仅有助于交通检测，在机动车安全，执法机关和商业应用中也大有益处。

本文提出了一种适于马来西亚车辆的车牌定位和识别系统。

此系统基于数字图像处理开发，且可以方便的应用于商业泊车系统并为停车服务提供文本记录，保障车库安全，并且可以预防车辆盗窃事件。

本文中提出的车牌定位算法基于一个形态学和改进的霍夫变换方法处理得到，车牌识别通过使用前向传播和反向传播人造神经网络。

一个复杂室外环境中捕捉到的589张图片的成功识别率为95%。

关键词：车牌，霍夫变换，反向传播，定位，字符分割识别和自动改正。

1.引言：通常来说，要给自动车牌定位和识别系统（ALPR）由三个模块组成；车牌定位，字符分割和光学字符识别模块（图.1）。

图.1.传统ALPR系统的流程图数字图像的车牌定位一般是通过使用边缘提取，直方图分析，形态算子或霍夫变换实现。

边缘提取通常比较简单和快捷。

但是，对方对噪声敏感。

如果车牌是由直线段组的则使用霍夫变换可以得到很好的结果。

但是，需要车牌的轮廓明显。

且需要很大的内存空间和相当长的运算时间。

另一方面，基础的直方图处理不能处理有大量噪声和倾斜的车牌。

最后：使用形态学方法处理不易受到噪声的影响，但是执行起来很慢。

单独使用这些技术不足以满足现代系统的需求。

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。

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。

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。

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。

车牌识别外文翻译讲解学习

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:收集于网络，如有侵权请联系管理员删除(1) Appearance variation caused by the change of image capturing conditions.(2)Style variation from one nation to another.(3)Style variation when the government releases new LP format. We summed 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 canresult in the change of LP style or appearance and then affectthe 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 morethan 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 abilityof 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收集于网络，如有侵权请联系管理员删除nation to another, by defining the four factors as parameters. Users can constrain the scope of a parameter and at the sametime the method will adjust itself so that the recognition canbe 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 onthe 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 theimage 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 Houghtransformation .Edge analysis is the most straightforward method with low computation complexity and good extensibility. Compared with edgebased algorithms, colorbased algorithms depend more on收集于网络，如有侵权请联系管理员删除the application conditions. Since LPs in a nation often have several predefined 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 complexitywill 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 arefurther 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收集于网络，如有侵权请联系管理员删除binarization algorithms, global or local methods are often used to 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收集于网络，如有侵权请联系管理员删除important points, for example, the corner points, in thetemplate to emphasize the different characteristics of the characters. Invariance of feature points is also considered inthe template matching method to improve the robustness. The disadvantage is that it is difficult to define new template bythe 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 maydrop 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 cantreat the cases of changes of illumination, vehicle speed,routes and backgrounds, which was realized by developing new收集于网络，如有侵权请联系管理员删除detection and segmentation algorithms with robustness to the illumination and image blurring. The performance of the methodis 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 descriptionof 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 concernsthe 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 stylesto others.多类型车牌识别配置的方法收集于网络，如有侵权请联系管理员删除自动车牌识别（LPR）在过去的几十年中的实用技术。

外文翻译

本科生毕业设计外文翻译题目车牌定位识别系统姓名金施焱学号 411109060316 学院信息工程学院专业电子信息工程指导教师甄丽平外文资料原文Building an Automatic Vehicle License-PlateRecognition SystemAbstract—Due to a huge number of vehicles, modern cities need to establish effectively automatic systems for traffic management and scheduling.One of the most useful systems is the Vehicle License-Plate (VLP) Recognition System which captures images of vehicles and read these plates’ registration numbers automatically.In this paper, we present an automatic VLP Recognition System, ISeeCarRecognizer, to read Vietnamese VLPs’ registration numbers at traffic tolls.Our system consists of three main modules: VLP detection, plate number segmentation, and plate number recognition.In VLP detection module, we propose an efficient boundary line-based method combining the Hough transform and Contour algorithm.This method optimizes speed and accuracy in processing images taken from various positions. Then, we use horizontal and vertical projection to separate plate numbers in VLP segmentation module.Finally, each plate number will be recognized by OCR module implemented by Hidden Markov Model.The system was evaluated in two empirical image sets and has proved its effectiveness (see section IV) which is applicable in real traffic toll systems. The system can also be applied to some other types of VLPs with minor changes.I. INTRODUCTIONThe problem of VLP recognition is a very interesting butdifficult one.It is very useful for many trafficmanagement systems.VLP recognition requires some complex tasks, such as VLP detection, segmentation and recognition.These tasks become more sophisticated when dealing with plate images taken in various inclined angles orplate images with noise.Because this problem is usually used in real-time systems, it requires not only accuracy but also fast processing.Most VLP recognition applications reduce the complexity by establishing some constrains on the position and distance from the camera to vehicles, and the inclined angles.By that way, the recognition rate of VLP recognition systems has been improved significantly.In addition, we can gain more accuracy by using some specific features of local VLPs, such as the number of characters, the number of rows in a plate, or colors of plate background, or the ratio of width to height of a plate .II. RELATED WORKThe problem of automatic VLP recognition has been studied since 1990s.The first approach was based on characteristics of boundary lines.The input image was first processed to enrich boundary lines’ information by some algorithms such as the gradient filter, and resulted in an edging image.This image was binarized and then processed by certain algorithms, such as Hough transform, to detect lines.Eventually, couples of 2-parallel lines were considered as a plate-candidate [4][5].Another approach was morphology-based [2].This approach focuses on some properties of plate images such as their brightness,symmetry, angles, etc.Due to these properties, this method can detect the similar properties in a certain image and locate the position of license plate regions.The third approach was texture-based [3].In this approach, a VLP was considered as an object with different textures and frames.The texture window frames of different sizes were used to detect plate-candidates.Each candidate was passed to a classifier toconfirm whether it is a plate or not.This approach was commonly used in finding text in images tasks.In addition, there have been a number of other methods relating to this problem focusing on detecting VLP in video data.III. THE PROPOSED SYSTEMOur system, ISeeCarRecognizer, consists of four modules: Pre-processing, VLP detection, character segmentation, and optical character recognition (OCR), in which the last three modules deal with three main problems of a VLP recognition domain.The VLP detection module receives images which have been processed by the preprocessing module – the first input module of this system.The resulted images of this module are sent to the segmentation module.The segmentation module segments plate-images into separate characterimages.These character-images are then recognized by the OCR module and the final results are ASCII characters andnumbers in plates.A. PreprocessingImages taken from camera were processed by the preprocessing module.The purpose of this module was to enrich the edge features.Because our detection method bases on the boundary features, it will improve the successful rate of the VLP detection module.The algorithms sequentially used in this module are graying, normalizing and histogram equalization.After having obtained a greyscale image, we use Sobel filters to extract the edging image, and then thresholds the image to a binary one.We used the local adaptive thresholding algorithm for the binarization step.Especially, we develop an algorithm based on dynamic programming to optimize its speed and make it suitable to real-time applications [1].The resulted images are used as inputs for the VLP detection module.B. VLP Detection AlgorithmIn boundary-based approach, the most important step is to detect boundary lines.One of most efficient algorithms is Hough transform applying to the binary image to extract lines from object-images.Then we look for two parallel lines, whose the contained region is considered platecandidates.However, the drawback of this approach is that the execution time of the Hough transform requires too much computation when being applied to a binary image with great number of pixels.Especially, the larger image the slower the algorithm is. The speed of the algorithm may beimproved by thinning image before applying the Houghtransform.Nevertheless, the thinning algorithm is also slow. This limitation makes the approach unsuitable for real time traffic management systems.The algorithm we used in this system is the combination of the Hough Transform and Contour algorithm which produces higher accuracy and faster speed so that it can be applied to real time systems.1) Combine Hough Transform and Contour Algorithm for Detecting VLP Our approach is as follows: from the extracted edging image, we use the contour algorithm to detect closed boundaries of objects.considered as a plate-candidate.Since there are quite few (black) pixels in the contour lines, the transformation of these points to Hough coordinate required much lesscomputation. Hence, the speed of the algorithm is improved significantly without the loss of accuracy . However, some plates may be covered by glasses ordecorated with headlights.These objects may also have the shape of two interacted 2-parallel lines, and therefore, arealso falsely detected as plate-candidates. To reject suchincorrect candidates, we implement a module forevaluating whether a candidate is a plate or not.2) Plate-Candidates Verification From the two horizontal lines of a candidate, we can calculate exactly how inclined it was from horizontal coordinate. Then we apply a rotate transformation to adjust it to straight angle. After processed, these straight binary plate-candidate regions were passed to a number of heuristics and algorithms for evaluating.Our evaluating plate-candidates algorithm bases on two main steps, which are taken respectively. The two steps are:(a) evaluate the ratios between the heights and the widths of the candidates, (b) use horizontal crosscuts to count the number of cut-objects in the candidates.In this stage, we check and only select out candidates that have the ratios of width to height satisfying pre-defined constraint: minWHRatio < W/H < maxWHRatioSince there are two main types of Vietnamese plates: 1-row and 2-row , we have two adequate constraints for two types.3.5 < W/H < 4.5 with 1-row plate-candidates0.8 < W/H < 1.4 with 2-row plate-candidatesThose candidates which satisfied one of the two aboveconstraints are selected and passed to the nextevaluation.Evaluate by using horizontal crosscutsIn this stage, we use two horizontal cuts and then count the number of objects that are cut by these crosscuts. A candidate will be considered as a plate if the number of cut objects is in the given range chosen suitably for each plate type by experiments .This number must be in the approximate range of the number of characters in a VLP, we have two appropriate constraints for two types of Vietnamese plates:Preprocessing OCR Segmentation. Images taken VLP Detection from camera License-patecharacters:4≤N≤8with 1-row plate-candidates 7≤N≤16 with 2-row plate-candidates With N is the number of cut-objects.The candidates that satisfied one of the two above constraints are selected as the final result. In our system, we implemented two hoziontal cuts at 1/3and 2/3 of plate-candidate’s height. The average of number of cut objects will be calculated. This evaluation helps to identify the correct plate-candidates.C. SegmentationTo correctly recognize characters, we have to segment a binary plate image to set of images which only contain one license character. These character images will be passed to the OCR module for recognizing. The common algorithm for this task is applying projections. However, in some cases, it does not work correctly.We will now describe our approach in segmentation by adding some enhancements to this method. We use a horizontal projection to detect and segment rows in 2 row plates. Because binary plate images were adjustedtheir inclined angles to zero, the result of row segmentation is nearly perfect. The positions with minimum values of horizontal projection are the start or the end of a row in plate.Different form row segmentation, character segmentation is more difficult due to many reasons such as stuck characters, screws, and mud covered in plates. These noise things cause the character segmentation algorithm using vertical projection to have some mistakes. In some worst cases of bad quality plate images, a character can be segmented into two pieces.We apply several constraints of ratio of the height to the width of a character. We search for the minimum values in the vertical projection and only the minimum positions which give cut pieces satisfied all predefined constraints are considered as the points for character segmentation. By this enhancement,we have achieved better results in this task. After this step,we have a list of character candidates. Not all of the candidates are actually images of characters. By that time, wecan re-evaluate whether a plate candidate is a plate or not by checking the number of characters of candidates. In Vietnam, a plate contains only 7 or 8 characters . The final plate candidates, together with their list of characters are passed to the OCR module for recognizing.D. Hidden Markov Model for OCR In this system, we use the HMM model for character recognition. The features which we used in this model are the ratio of foreground pixels in a window. We use a window with the size of 9×9, and scan this window in the image from left to right and top to bottom These windows can overlap each other by two thirds of their size. By this way, we have a feature vector which includes 196 values. In the recognition module, we need to classify a character image into one of 36 classes (26 alphabet letters: A, B, C…and 10 numeric characters: 0, 1, 2…).To train our model, we use training sets which were extracted from images of VLPs.The number of samples for every class is about60.These samples were extracted from real VLP images with a little noise, so after well trained, the model can recognize exactly plates with the similar types of noise.In the last step, we use some specific rules of Vietnamese VLPs to improve accuracy.We learned that the third character in plate must be a letter, the fourth is sometimes a letter but usually a number, and the other positions are surely numbers.IV. EMPIRICAL EV ALUATIONOur system was evaluated with two sets of Vietnamese vehicles’ plates. Images were taken by a Sony DC350 digital camera, with size of 800x600 pixels, in different places and times. We use Microsoft Visual C++ 6.0, run on HP Workstation X2000 Pentium IV, 1.4 GHz, 512 MB RAM, Windows XP OS.V. CONCLUSIONS AND FUTURE WORKThe system performs well on various types of Vietnamese VLP images, even on scratched, scaled plate images. In addition, it can deal with the cases of multiple plates in the same image, or different types of vehicles such as motorbike plates, car plates or truck plates. However, it still has a few errors when dealing with bad quality plates. We are working on a number of algorithms in the preprocessing module. The purpose is to detect regions that are likely plate regions first and thus to reduce the computation cost of the VLP detection algorithm. In addition, we intend to combine a number of texture-based approachs, and machine learning methods to evaluate platecadidates. We believe these will improve the accuracy and the speed of the algorithm furthermore. Index Terms—Vehicle License-Plate Recognition, Real-time System, Hough Transform, Contour Algorithm.译文建立一个车牌自动识别系统摘要由于目前大量的汽车，现代城市交通管理需要建立有效的自动管理系统。

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。

[开题报告]车牌识别系统中定位算法的研究

毕业论文（设计）开题报告（含文献综述、外文翻译）题目车牌识别系统中定位算法的研究姓名黄泽学号3060433088专业班级06自动化1班指导教师崔家林分院信息科学与工程分院开题日期2010年 3 月25日第1章文献综述1.1 国内外现状汽车牌照识别技术(License Plate Recognition, LPR)是智能交通系统(ITS)的重要组成部分，多应用在电子计费领域。

LPR 系统是一个以特定目标为对象的专用计算机视觉系统，该系统能从一幅图像中自动提取车牌图像，自动分割字符，运用模式识别、人工智能技术，实时准确地自动识别出车牌的数字、字母及汉字字符，使得车辆的电脑化监控和管理成为现实。

常用的LPR 识别技术有IC卡识别技术、条形码识别技术和图像处理识别技术。

基于图像处理技术的LPR 系统无需在车上额外安装条形码或者IC卡，因而不必改造现有的车辆系统，相对其他两种识别技术来说适用面广，更容易普及[1]。

车牌识别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,该技术能经过图像抓拍、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的方法,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。

其在交通监视和控制中占有很重要的地位。

车牌识别技术的研究最早出现在20 世纪80 年代,这个阶段的研究没有形成完整的系统体系,而是就某一具体的问题进行研究,通常采用简单的图像处理方法来解决。

识别过程是使用工业电视摄像机( Industrial TV Camera) 拍下汽车的正前方图像,然后交给计算机进行简单处理,并且最终仍需要人工干预[2]。

从20 世纪90 年代初，国外的研究人员就已经开始了对车牌识别的相关研究，其中具有代表性的工作有：R.Mullot等开发的一种可以同时用于集装箱和普通车辆的车牌识别系统，该系统主要是利用文字的纹理在车辆图像中的共性进行定位与识别。

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)

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

车牌定位-本科毕业设计论文

车牌识别系统毕业论文

车牌识别外文文献翻译中英文

实时的车牌识别系统 中英文

车牌照识别系统设计与实现毕业设计论文

汽车牌照自动识别系统中英文对照外文翻译文献

车牌识别毕业论文

车牌识别系统中车牌定位及分割技术研究

车牌定位技术研究毕业设计论文含开题报告

文献翻译+原文

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)

车牌识别系统论文

车辆图像预处理和车牌定位的方法研究 中英文对照翻译

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)

车牌识别英文文献2翻译

车牌识别外文翻译讲解学习

外文翻译

车牌识别英文文献1翻译

[开题报告]车牌识别系统中定位算法的研究

实时的车牌识别系统中英文

车辆图像预处理和车牌定位的方法研究中英文对照翻译