License Plate Location And Character Segmentation Algorithm

As the coming of the 21st century, the communication and transportation in our country have developed themselves rapidly and have stepped into the field of infrastructure, which the government would make great efforts to support. It makes our life become more and more convenient, but it also brings a huge challenge to the urban traffic because of more and more vehicles. Intelligent traffic emerges and becomes the main future development as the times require. As the critical technique, the license plate recognition (LPR) plays an important role in intelligent traffic. With the help of LPR, the vehicles can be supervised in all-weather automatically. It not only increases efficiency but also saves manpower and material resources. So it could be used widely in the occasions of intersection, expressway, parking place and so on.The whole license plate recognition system is composed of software system and hardware system. The software system can be divided into three parts according to the image processing technology: the license plate location, the segmentation of license plate characters and the recognition of characters. This article mainly discusses and researches on the algorithm of license plate location and segmentation of license plate characters.1. the algorithm of license plate location. This part is the first step and it plays a fundamental role in the whole system. The existing algorithm can only make the plate located, which has white lettering on a blue background. In view of the problem, this paper proposes a new locating method based on edge texture and edge-color pair. In this algorithm, we firstly obtain the edge of license plate with Sobel operator. Then remove the disturbed edge according to the character of edge texture and edge-color pair. At last, we get the connected region of candidate license plate by slip window and extract the LP. The operator of removing disturbed edge for double times not only makes the subsequent process easy but also minimizes the occurrence of pseudo plate greatly. In the process of precise location, the slant LP needs to be corrected. The existing method based on Hough transform often failed because of serious disturbance in LP. In our paper, we make some improvements to the existing algorithm. Lastly, in order to avoid the influence by imprecise left and borders of LP, which maybe make subsequent character segmentation failed. This paper combines template and first-order difference to get the accurate left and right borders of LP.2. The algorithm of segmentation of license plate characters. Before the character segmentation, we need to normalize the LP to state of black background with white text. Because of the adhesion between characters, the existing method will fail in the process of normalization. In our paper, the morphology method is employed to solve this problem. In order to improve the character segmentation accuracy, we try a new method based on support vector machine (SVM). This method mainly uses projection feature of LP to train and classify. In the process, we also further remove the pseudo plate. At the same time, we complete the segmentation of the double line LP.Our entire algorithm is fulfilled on the platform of MALAB. This paper uses a lot of images taken in the intersection to test the proposed approach. From the test result we know that the accuracy rate of license plate location is 95.1%, the accuracy of license plate segmentation is 95%.