【正文】 ms 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 bees 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 promise 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 learningbased 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. SVMbased 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 learningbased 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 learningbased 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 meth。同時，也對那些在大學四年里，教會了我我專業(yè)知識，教會了我如何學習，教會了我如何做人的老師們，表達我由衷的謝意，正是由于他們的監(jiān)督和教導，我才能在各方面取得顯著的進步，在這里，愿所有的老師及師兄師姐們身體健康！在這里，也要感謝陪我渡過大學四年時光的全體同學，和四年里基本上朝夕相處的舍友們，正是在他們的關(guān)心和幫助下，我才能順順利利的結(jié)束我的大學生活。在此，我非常感謝我的指導老師徐洋教授和其他教導過我的老師們以及幫助過我的師兄師姐們，他們在我完成論文的過程中給予了非常大的幫助。一種基于數(shù)據(jù)流修正的自適應模板匹配法[15]，就能夠避免了設置固定模板的缺陷，使模板匹配的效率得到提高。模板的制作相當?shù)年P(guān)鍵，如若模板制作得不夠精確，那么將很難正確識別出結(jié)果。因此為進一步提高字符分割的準確率，我們應該對這種方法進行改進，而可以考慮的是模板匹配—垂直投影結(jié)合法[14]，其能夠更好地解決車牌字符粘連、斷裂等問題。在字符識別方面，采用的是垂直投影法。而且若是車牌顏色與背景整體顏色相近的話，很難正確定位出車牌區(qū)域。結(jié) 論在本次設計中基本上實現(xiàn)了對與車牌自動識別系統(tǒng)的Matlab仿真，并且有著不錯的效果，但不可忽略的是在這個程序中有很多地方仍然需要改進。在其中，講解了每個算法的定義及原理，也說明了每個函數(shù)的作用及其調(diào)用格式。 總之，盡管目前牌照字符的識別率還不理想，但是只要在分割出的字符的大小、位置的歸一化，以及嘗試提取分類識別能力更好的特征值和設計分類器等環(huán)節(jié)上再完善，進一步提高識別率是完全可行的。模板的制作很重要，必須要用精確的模板，否則就不能正確的識別。 最后將分割出來的字符運用模板匹配的方法與模板字符進行匹配，將其與模板字符相減，得到的0越多那么就越匹配。分割采用的方法為尋找連續(xù)有文字的塊，若長度大于某閾值T，則認為該塊有兩個字符組成，需要分割。其難點在于噪聲合字符粘連，斷裂對字符的影響，因此必須先將定位后的車牌進一步處理。再根據(jù)車牌底色等有關(guān)的先驗知識，采用