车牌识别外文翻译.doc

中英文翻译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 aremainly developed for specific nation or special LP formats. In Ref. the authors focus on recognizing Greek LPs by proposing new segmentation and recognition algorithms. The characters on LPs are alphanumerics with several fixed formats. In Ref. Zhang et al. developed a learning-based method for LP detection and character recognition. Their method is mainly for LPs of Korean styles. In Ref. optical character recognition (OCR) technique are integrated into LPR to develop general LPR method, while the performance of OCR may drop when facing LPs of poor image quality since it is difficult to discriminate real character from candidates without format supervision. This method can only select candidates of best recognition results as LP characters without recovery process. Wang et al. developed a method to recognize LPR with various viewing angles. Skew factor is considered in their method. In Ref. the authors proposed an automatic LPR method which can treat the cases of changes of illumination, vehicle speed, routes and backgrounds, which was realized by developing new detection and segmentation algorithms with robustness to 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. 多类型车牌识别配置的方法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:自动车牌识别（LPR）在过去的几十年中的实用技术。许多应用，如自动收费，犯罪的追求和交通执法，已从中受益。虽然一些新技术，如RFID（无线射频识别），WSN（无线传感器网络），等，已提出了汽车身份识别，车牌图像数据仍因其方便、成本低，在目前的智能交通系统不可缺少的技术。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。检测步骤大致分类LP和非LP区域分割步骤，将符号/字符从彼此在一个LP，只有准确的轮廓，每个字符图像块左为识别和识别步骤，最后将灰度图像块转换成字符/符号通过预定义的识别模型。虽然车牌识别技术有着很长的研究历史，它仍然是推动各种要求而产生的，最常见的一个是LP风格的变化，例如：(1) Appearance variation caused by the change of image capturing conditions.（1）通过图像采集条件的变化引起的外观变化。Style variation from one nation to another.风格的变化从一个国家到另一个。Style variation when the government releases new LP format.风格的变化时，政府发布新的LP格式。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.我们将其总结为四个因素，即旋转角度，线数，性格类型和格式，在对实际数据的多样式的LP特征综合分析。一般来说，上述四个因素的任何变化都会导致LP的风格或外表的变化进而影响检测，分割和识别算法。如果LP有一个大的旋转角度，水平LP分割和识别算法可能不工作。如果有一个以上的在一个LP的特征线，更多的线分离算法分割处理前需要。与人的性格类型的变化时，我们采用的方法从一个国家到另一个，有能力重新定义识别模型是必要的。更甚的是，LP风格的变化需要调整的方法本身，分割和识别候选字符可以匹配最好用一个LP格式。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.已经提出了几种方法，近年来跨国LPS或LPS多而很少全面解决上述因素的风格适应问题。他们中的一些人只要求处理跨国LPS的能力通过重新定义的检测和分割规则或识别模型。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.在本文中，我们提出了一个可配置的车牌识别方法是从一个到另一个适合的风格，特别是从一个国家到另一个，通过定义四个因素作为参数。用户可以约束的参数范围，同时该方法将自我调整，这样可以更快、更准确的识别。类似于现有的车牌识别技术，我们还提供详细的检测，分割和识别算法。不同的是，我们强调了车牌识别和可扩展性的方法而不是multistyle LPS各算法性能的可配置的框架。In the past decades, man