外文翻译 ---建立一个自动车辆车牌识别系统

附件1：外文资料翻译译文

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（心室晚电位）辨识系统，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

心室晚电位识别的问题是一个非常有趣，但

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，如心室晚电位检测，分割和承认。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板噪音板的图像。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，心室晚电位识别率

识别系统已得到明显改善.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.由于这些特性，这种方法可以检测出图像中的某些相似的性质和找到车牌区域的位置。第三种方法是基于纹理[3]。在这种方法中，一个心室晚电位被认为是

一个对象和不同的纹理帧。大小不同的纹理窗框用于检测板的候选人。每个人获得通过一个分类，以确认它是否是一个盘子或没有。这常用的方法是寻找图像中的文字任务。此外，已经出现了一些其他有关这个问题的方法上注重检测心室晚电位

在视频数据。

三.拟议的系统

我们的系统，ISeeCarRecognizer，由四个模块：前处理，心室晚电位检测，字符分割，和光学字符识别（OCR），在其中最后三个模块处理三个主要问题一个心室晚电位识别域。是VLP检测模块接收到的图像有被处理的预处理模块 -第一个输入该系统的模块。这个模块的结果图像发送到分段模块。分割段模块板的图像，成为独立的characterimages。这些字符的图像，然后认可光学字符识别模块和最终结果是ASCII字符和板块中的数字。

1.预处理

从相机拍摄的图像进行处理的预处理模块。本模块的目的是丰富的边缘特征。由于我们的检测方法在边界上的基地功能，它可以改善成功率的心室晚电位检测模块。该算法在此模块顺序使用的老龄化，规范化和直方图均衡。在得到一个灰阶图片中，我们使用过滤器来提取索贝尔边缘图像，然后以一个二进制阈值的一个图像。我们用于局部自适应阈值算法二值化的一步。特别是，我们发展一种基于动态规划，优化其速度，使其适用于实时应用[1]。图像的结果被用作心室晚电位检测模块的输入。

2.心室晚电位检测算法

在边界为基础的方法，最重要的步骤是检测边界线。最有效的算法之一是Hough变换申请提取的二进制映像线从对象的图像。然后我们找两平行线，其包含的区域被认为platecandidates。然而，这种方法的缺点是，霍夫变换的执行时间需要太多多的运算时，被应用到一个二进制图像与大量的像素。特别是，较大的图像慢的算法。该算法的速度可能会通过细化图像改进，然后再应用霍夫变换。然而，细化算法也慢。这种限制使这种方法不适合实时交通管理系统。该算法在本系统中我们采用的是组合

Hough变换的算法和轮廓产生更高的精度和更快的速度，它可以适用于实时系统。1）结合Hough变换和轮廓算法心室晚电位检测

我们的做法是：从提取的边缘图片中，我们使用封闭的轮廓检测算法边界的对象。这些轮廓线改造到霍夫协调，找到两个平行线互动（2 -平行线之一成立回另两平行线并建立一个平行四边形表对象）是作为板候选人考虑。由于有相当少（黑）在等高线的像素，转化这些需要协调霍夫点少得多计算。因此，该算法的速度提高没有明显的精度损失。然而，有

些板块可能会覆盖眼镜或装饰灯。这些对象还可能有

形状两个相互作用二平行线，因此是错误地检测为板候选人。要拒绝这样不正确的候选人，我们评估一个模块的实施无论候选人是板或没有。

2）板考生核查

从两个候选人的水平线，我们可以如何准确地计算出它从水平倾斜坐标。然后，我们应用旋转转换调整它为平角。经过处理，这些标准二进制板候选区域被传递给一个号码启发式检测和评估算法。我们的评价板候选人在两个算法基地主要步骤，分别采取。这两个步骤是：

（1）评价之间的高度和宽度的比例候选人，

（2）使用水平横切来计算数切入候选人的对象。

我们只选择了检查和候选人有宽度与高度之比满足预先定义约束：minWHRatio<宽/高

利用水平评价横切。在这个阶段，我们使用两个水平削减和再算上该由这些横切削减对象的数量。一候选人将被视为一个盘子，如果数量的减少选择对象为每个板块在一定范围内适当

通过实验类型。这个数字必须在数量大致范围在一类病毒颗粒的字符，我们有两个合适的约束两个越南板类型：预处理OCR的分割拍摄的图像心室晚电位检测从相机许可证帕泰字符:4≤1≤8?排板候选人7≤2≤n时排板候选人16与N是禁对象的数量。候选人是满足了上述两个约束选定为最终结果。在我们的制度，我们实施的1 /3两hoziontal削减和2 / 3的板候选人的高度。平均的数目切对象将被计算。这种评估有助于确定正确的板候选人。

(3).分割

要正确认识字，我们要一车牌图像二值图像的设置只包含一车牌字符。这些形象将被传递到对于OCR的识别模块。这个任务常见的算法是运用预测。然而，在一些情况下，无法正常工作。我们现在将描述我们在分割方法添加一些增强此方法。

我们用一个水平投影检测和部分行排在二板。因为二进制图像进行了调整板他们的倾斜角度为零，分割结果的行几乎是完美的。与最低值的位置水平投影是启动或在最后一排板。不同形式的行分割，字符分割更为困难，因为许多原因，如卡字符，螺丝，和泥覆盖板。这些噪音事情的原因使用的字符分割算法垂直投影，有一些错误。在一些最严重的图像质量差

板的情况下，一个字符可以分割成两部分。我们应用的若干制约因素比到一个字符的宽度高度。我们寻求的最低值在垂直投影，只有这给削减最低位置件满足所有预定义的限制被认为是

字符分割点。通过此增强，我们在这项任务中取得了较好的效果。经过这一步,我们有一个人物候选人名单。并不是所有的考生实际上是人物形象。

到那时，我们可以重新评估候选人是否板是一盘或不检查的字符数候选人。在越南，一盘只包含7或8字符。最后一盘的候选人，连同与他们的字符列表传递到OCR模块负责确认。

(4).用于光学字符识别隐马尔可夫模型

在这个系统中，我们使用的字符的HMM模型承认。我们的特点，在此模型中使用的在窗口中的比例前景像素。我们使用的9× 9大小的窗口，这个扫描在图像窗口中，从左至右，从上到下

这些窗口可以由两个互相重叠三分之二的大小。通过这种方式，我们有一个特征向量其中包括196值。

在识别模块，我们需要一个字符分类成一个形象的36个班（26个英文字母：甲，乙，丙...

和10个数字字符：0，1，2 ...）。要培养我们的模型，我们使用的训练，是从图像中提取套

病毒样颗粒。对每类样本数约为60.These提取样品图像实时心室晚电位一点点的噪音，所以在良好的训练，该模型可以正是认识到板的同类型的噪音。在最后一步，我们使用越南的一些具体规则病毒样颗粒以提高准确性。我们了解到，第三次在车牌字符必须是字母，四是有时信，但通常是一个数字，其他位置当然是数字。

四.实证评价

我们的系统进行了评价与越南两个套车辆的车牌。图像由索尼DC350数码相机，具有800x600像素大小，在不同地点和时间。我们使用Microsoft Visual C++ 6.0，运行惠普工作站X2000奔腾IV，1.4千兆赫，512 MB的的RAM，Windows XP操作系统。

五.结论和未来工作

该系统运行良好的越南各类心室晚电位的图像，甚至抓伤，缩放板的图像。在此外，它可以处理多个板块中的案件相同的图像，或不同类型的车辆，如摩托车板，汽车板或车板。然而，它仍然有几个错误在处理劣质板材。

我们正在数的算法在预处理模块。其目的是探测地区的第一盘地区可能，从而减少计算成本的心室晚电位检测算法。在此外，我们打算结合的纹理为基础的数方法，和机器学习的方法来评价platecadidates。我们相信，这些将提高信息的准确性和该算法的速度进一步。

索引词:车载车牌识别，实时系统，Hough变换，轮廓算法。

附件2：外文原文（复印件）

Building an Automatic Vehicle License-Plate

Recognition System

Abstract—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. INTRODUCTION

The 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 WORK

The 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 to confirm 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 SYSTEM

Our 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. Preprocessing

Images 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 Algorithm

In 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 for evaluating 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 ≤ 8 with

1-row plate-candidates7 ≤ 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. Segmentation

To 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, we can 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 ALUATION

Our system was evaluated with two sets of Vietnamese vehicles’ plates. Images we re 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 WORK

The 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.