車牌識(shí)別外文

1、中英文翻譯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 . Al

2、though 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. LP

3、R 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 imag

4、e 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 on

5、e 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 angl

6、e, 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. I

7、f 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

8、 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

9、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 recogni

10、tion 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

11、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 LP

12、s 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

13、 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, grad

14、ient (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 an

15、alysis 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

16、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. This kind of method can be affected a lot by lighting conditions. To w

17、in 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 mult

18、iorientations 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 o

19、f 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 optim

20、ization. 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 vert

21、ical 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 f

22、rom 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 segm

23、entation 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

24、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 ne

25、twork (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

26、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 ass

27、ign 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 difficu

28、lt 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 s

29、pecial 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

30、 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 wi

31、thout 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 LP

32、R 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. S