中英文翻译
A configurable method for multi-style licen plate recognition
Automatic licen 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 nsor network), etc., have been propod 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: licen plate detection, character gmentation and character recognition. The detection step roughly classifies LP and non-LP regions, the gmentation step parates 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 predefine
d recognition models. Although LPR technique has a long rearch history, it is still driven forward by various arising demands, the most frequent one of which is the variation of LP styles, for example:
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匠气>都是戴茜惹的祸(1) Appearance variation caud by the change of image capturing conditions.
我们和地球英文翻译(2) 心理调节Style variation from one nation to another. 新版中日交流标准日本语
(3) Style variation when the government releas new LP format.
We summed them up into four factors, namely rotation angle, line number, character type and format, after comprehensive analys 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, gmentation or recognition algorithms. If one LP has a large rotation angle, the gmentation and recognition algorithms for horizontal LP may not work. If there are more than one character lines in one LP, additional line paration algorithm is needed before a gmentation process. Wi
th 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 itlf so that the gmented and recognized character candidates can match best with an LP format.
Several methods have been propod 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 gmentation rules or recognition models.
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In this paper, we propo 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. Urs can constrain the scope of a parameter and at the same time the method will adjust itlf so that the recognition can be faster and more accurate. Similar to existing LPR techniques, we also provide details of detection, gmentation and recog
nition algorithms. The difference is that we emphasize on the configurable framework for LPR and the extensibility of the propod method for multistyle LPs instead of the performance of each algorithm.
In the past decades, many methods have been propod for LPR that contains detection, gmentation and recognition algorithms. In the following paragraphs, the algorithms and LPR methods bad on them are briefly reviewed.
LP detection algorithms can be mainly classified into three class according to the features ud, namely edgebad algorithms, colorbad algorithms and texture-bad algorithms. The most commonly ud method for LP detection is certainly the combinations of edge detection and mathematical morphology .In the 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 den edges or to describe the den edges in LP regions by a Hough transformation .Edge analysis is the most straightforwar
d method with low computation complexity and good extensibility. Compared with edgebad algorithms, colorbad algorithms depend more on the application conditions. Since LPs in a nation often have veral predefined colors, rearchers have defined color models to gment 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 fal positive rates, texture classification has been ud for LP detection. In Ref.Kim et al. ud an SVM to train texture classifiers to detect image block that contains LP pixels.In Ref. the authors ud Gabor filters to extract texture features in multiscales and multiorientations to describe the texture properties of LP regions. In Ref. Zhang ud X and Y vulderivative 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 the methods the computation complexity will limit their usability. In addition, texture-bad algorithms may be affected by multi-lingual factors.
Multi-line LP gmentation algorithms can also be classified into three class, namely algorithms bad on projection， binarization and global optimization. In the projection al
材料科学与工程就业gorithms, gradient or color projection on vertical orientation will be calculated at first. The “valleys”第一次约会说什么 on the projection result are regarded as the space between characters and ud to gment characters from each other. Segmented regions are further procesd by vertical projection to obtain preci bounding boxes of the LP characters. Since simple gmentation methods are easily affected by the rotation of LP, gmenting the skewed LP becomes a key issue to be solved. In the binarization algorithms, global or local methods are often ud to obtain foreground from background and then region connection operation is ud to obtain character regions. In the most recent work, local threshold determination and slide window technique are developed to improve the gmentation performance. In the global optimization algorithms, the goal is not to obtain good gmentation result for independent characters but to obtain a compromi of character spatial arrangement and single character recognition result. Hidden Markov chain has been ud to formulate the dynamic gmentation of characters in LP. The advantage of the algorithm is that the global optimization will improve the robustness to noi. And the disadvantage is that preci format definition is necessary before a gmentation process.
