多类型车牌识别配置的方法毕业论文外文翻译.docx

一、英文原文：A configurable method for multi-style license plate recognition Automatic license plate recognition (LPR) has been a practical technique in the past decades. Numerous applications, such as automatic toll collection, criminal pursuit and traffic law enforcement , have been benefited from it . Although some novel techniques, for example RFID (radio frequency identification), WSN (wireless sensor network), etc., have been proposed for car ID identification, LPR on image data is still an indispensable technique in current intelligent transportation systems for its convenience and low cost. LPR is generally divided into three steps: license plate detection, character segmentation and character recognition. The detection step roughly classifies LP and non-LP regions, the segmentation step separates the symbols/characters from each other in one LP so that only accurate outline of each image block of characters is left for the recognition, and the recognition step finally converts greylevel image block into characters/symbols by predefined recognition models. Although LPR technique has a long research history, it is still driven forward by various arising demands, the most frequent one of which is the variation of LP styles, for example:(1) Appearance variation caused by the change of image 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 can result in the change of LP style or appearance and then affect the detection, segmentation or recognition algorithms. If one LP has a large rotation angle, the segmentation and recognition algorithms for horizontal LP may not work. If there are more than one character lines in one LP, additional line separation algorithm is needed before a segmentation process. With the variation of character types when we apply the method from one nation to another, the ability to re-define the recognition models is needed. What is more, the change of LP styles requires the method to adjust by itself so that the segmented and recognized character candidates can match best with an LP format. Several methods have been proposed for multi-national LPs or multiformat LPs in the past years while few of them comprehensively address the style adaptation problem in terms of the abovementioned factors. Some of them only claim the ability of processing multinational LPs by redefining the detection and segmentation rules or recognition models. In this paper, we propose a configurable LPR method which is adaptable from one style to another, particularly from one nation to another, by defining the four factors as parameters. Users can constrain the scope of a parameter and at the same time the method will adjust itself so that the recognition can be faster and more accurate. Similar to existing LPR techniques, we also provide details of detection, segmentation and recognition algorithms. The difference is that we emphasize on the configurable framework for LPR and the extensibility of the proposed method for multistyle LPs instead of the performance of each algorithm. In the past decades, many methods have been proposed for LPR that contains detection, segmentation and recognition algorithms. In the following paragraphs, these algorithms and LPR methods based on them are briefly reviewed. LP detection algorithms can be mainly classified into three classes according to the features used, namely edgebased algorithms, colorbased algorithms and texture-based algorithms. The most commonly used method for LP detection is certainly the combinations of edge detection and mathematical morphology .In these methods, gradient (edges) is first extracted from the image and then a spatial analysis by morphology is applied to connect the edges into LP regions. Another way is counting edges on the image rows to find out regions of dense edges or to describe the dense edges in LP regions by a Hough transformation .Edge analysis is the most straightforward method with low computation complexity and good extensibility. Compared with edgebased algorithms, colorbased algorithms depend more on the application conditions. Since LPs in a nation often have 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 complexity will limit their usability. In addition, texture-based algorithms may be affected by multi-lingual factors. Multi-line LP segmentation algorithms can also be classified into three classes, namely algorithms based on projection， binarization and global optimization. In the projection algorithms, gradient or color projection on vertical orientation will be calculated at first. The “valleys” on the projection result are regarded as the space between characters and used to segment characters from each other. Segmented regions are further processed by vertical projection to obtain precise bounding boxes of the LP characters. Since simple segmentation methods are easily affected by the rotation of LP, segmenting the skewed LP becomes a key issue to be solved. In the binarization algorithms, global or local methods are often 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 important points, for example, the corner points, in the template to emphasize the different characteristics of the characters. Invariance of feature points is also considered in the template matching method to improve the robustness. The disadvantage is that it is difficult to define new template by the users who have no professional knowledge on pattern recognition, which will restrict the application of the algorithm. Based on the abovementioned algorithms, lots of LPR methods have been developed. However, these methods are mainly developed for specific nation or special LP formats. In Ref. the authors focus on recognizing Greek LPs by proposing new segmentation and recognition algorithms. The characters on LPs are alphanumerics with several fixed formats. In Ref. Zhang et al. developed a learning-based method for LP detection and character recognition. Their method is mainly for LPs of Korean styles. In Ref. optical character recognition (OCR) technique are integrated into LPR to develop general LPR method, while the performance of OCR may drop when facing LPs of poor image quality since it is difficult to discriminate real character from candidates without format supervision. This method can only select candidates of best recognition results as LP characters without recovery process. Wang et al. developed a method to recognize LPR with various viewing angles. Skew factor is considered in their method. In Ref. the authors proposed an automatic LPR method which can treat the cases of changes of illumination, vehicle speed, routes and backgrounds, which was realized by developing new detection and segmentation algorithms with robustness to the illumination and image blurring. The performance of the method is encouraging while the authors do not present the recognition result in multination or multistyle conditions. In Ref. the authors propose an LPR method in multinational environment with character segmentation and format independent recognition. Since no recognition information is used in character segmentation, false segmented characters from background noise may be produced. What is more, the recognition method is not a learning-based method, which will limit its extensibility. In Ref. Mecocci et al. propose a generative recognition method. Generative models (GM) are proposed to produce many synthetic characters whose statistical variability is equivalent (for each class) to that showed by real samples. Thus a suitable statistical description of a large set of characters can be obtained by using only a limited set of images. As a result, the extension ability of character recognition is improved. This method mainly concerns the character recognition extensibility instead of whole LPR method. From the review we can see that LPR method in multistyle LPR with multinational application is not fully considered. Lots of existing LPR methods can work very well in a special application condition while the performance will drop sharply when they are extended from one condition to another, or from several styles to others. 二、英文翻译：多类型车牌识别配置的方法 自动车牌识别技术在过去的几十年中已成为一项实用的技术。许多的应用，如自动收费系统、犯罪的追捕和交通执法，已从中受益。虽然一些新的技术，比如RFID（无线射频识别），WSN（无线传感网络）等已经提出了汽车身份识别，但是车牌识别的图像数据技术仍因其方便、成本低的特点，在目前的智能交通系统中成为了一项不可缺少的技术。车牌识别系统一般分为三个步骤：车牌定位，字符分割和字符识别。检测步骤大致分为LP（车牌）和非LP（车牌）区域，分割步骤分割符号/字符彼此在同一个车牌，以便在识别时字符块从准确轮廓的左边开始，车牌识别的最后一步转换灰度图像块成字符/符号识别的预定义模式。虽然车牌识别技术有着很长的研究历史，但它仍然被各种产生的需求而驱动着，最常见的一个是车牌风格的变化。例如： （1 ）外观变化而引起的图像采集条件的变化。 （2）风格的变化从一个地区到另一个地区。 （3）当政府发布新的车牌格式时所引起的变化。我们将其概括为四个因素，即旋转角度，行数，字符类型和格式，在综合分析后，得到多样式的车牌实时数据。一般而言，上述四个因素的任何变化都可能会导致车牌风格和外观的变化，进而影响到检测，分割和识别方法。如果一个车牌有一个大的旋转角度，分割和识别算法对于横向车牌都将无法正常工作。如果一个车牌有多于一个字符线，附加行分离算法需要在一个分离进程前。当我们的方法适用于从一国到另一国时，随着字符类型的变化，有能力重新定义认识模型是必要的。更重要的是，车牌风格的变化需要方法来调整车牌本身，以致候选的分割和识别字符可以最好地匹配一个车牌格式。在过去的几年中，由于多国车牌或者多格式的车牌而提出了几种方法，但是他们中只有一些能全面解决在风格适应方面的问题。他们中的一些只要求有通过重新定义检测和分割标准或者识别模式来处理多国车牌的能力。在本文中，我们提出了一种可配置的车牌识别方法是适用从一个风格到另一个风格，特别是从一个国家到另一个国家的时候，通过所确定的四个因素来作为参数。用户可以限制参数的范围，并且同时方法还将进行自我调整，从而使识别可以更快、更准确。和现有的车牌识别技术类似，我们还提供详细的检测、分割和识别算法。与其不同的是，我们强调的是车牌识别系统可配置的框架和为多样式车牌而提出的可扩展性的方法，而不是每一种算法的性能。在过去的几十年中，我们已经提出了许多方法用于车牌识别包括检测，分割和识别算法。在下面的段落中，这些算法和车牌识别的方法以他们为基础，进行了简要回顾。车牌检测算法可以根据使用的特征主要分为三类，即基于边缘的算法，基于颜色特征的算法和基于纹理的算法。对车牌检测最常用的方法当然是边缘检测和数学形态学的组合。在这些方法中，梯度（边缘）是第一个从图像中提取的，随后的形态空间分析被用来与车牌区域和边缘建立关系。另一种方法是为找出密集的边缘区域或者形成车牌区域密集边缘的图形通过霍夫变换在图像坐标计算边缘。边缘分析是具有较低的计算复杂度和良好的扩展性的最简单的方法。与基于边缘算法相比，基于颜色特征的算法更依赖于应用条件。由于一个国家车牌往往有几种事先确定的颜色，研究人员以定义的颜色模型来分割区域从而确定车牌区域。这种方法照明条件将会对其影响很大。为了赢得较高的查全率和较低的误报率，纹理识别已被用于车牌检测。在文献中，吉姆等人用SVM来整理纹理分类以检测包含车牌像素的图像区域。作者采用高波滤波器来提取多尺度的纹理特性，和多方位的描述车牌区域的纹理特性。在文献中，张利用X和Y的导数特性，灰度值的变化和Adaboost算法分类来分割一张图像中的车牌区域和非车牌区域。还用小波特征分析方法来识别车牌区域。尽管这些方法性能良好，但是计算的复杂性却限制了他们的可用性。此外，基于纹理特征的算法也会受到多语言因素的影响。多线的车牌分割算法也可分为三类，即以投影法为基础的算法，二值化和整体优化算法。在投影算法上，在垂直方向的梯度或者色彩投影将先计算。“流域”在投影结果中被作为特征和用于分割字符之间的间隔。分割区域将通过垂直投影作进一步的处理以获得精确地车牌字符的边界框。因为简单的分割方法容易被车牌的旋转所影响，因此对倾斜的车牌进行分割已成为了一个亟待解决的关键问题。在二值算法中，整