Motor Vehicle License Plate Automatic Recognition System Of Algorithms,

Automatic Vehicle License Plate Recognition System is an important component of the Intelligent Transportation System. It plays an important role in mitigating and managing the increasing congestion in urban traffic and road transport. At present, the Automatic Vehicle License Plate Recognition System is not only used in car parks, highway tolls, residential vehicle management and other simple gate scenes, and also is used in some complex scenes such as urban crossroads which has a complex road conditions and a heavy traffic.In this paper, according to Digital Image Processing, Pattern Recognition and Statistical Learning Theory, the algorithms of license plate location and character recognition for simple gate scenes and urban crossroads in automatic Vehicle License Plate Recognition System are proposed. Major researches in this paper are as follows:1. Vehicle license plate location is to extract the license plate from which the vehicles images contain. In this paper, an algorithm for the license plate location based on the density and projection is put forward. After the image preprocessing, such as transformation from color images to gray images and gray stretch, calculate the first-order differential of the whole level region to get the first-order image, and then, the level position of plate can be located through the algorithm of wave merging and the median filtering. Next, using the vertical Sobel edge detection operator, the left and right boundary of plate can be located through the density. Finally, using the Hough transform to correct the located inclined plate for a better data source for character segmentation.2. License plate character recognition is to identify the corresponding characters from the character images which are from the character segmentation, and give the results in text form. In this paper, two algorithms of character recognition for two different scenes are used respectively, that is the One-against-One multi-class support vector machine algorithm for the gate scenes and the Adaboost algorithm combined with the One-against-One multi-class SVM for the crossroads. And in accordance with standards of China's existing license plate GA36-2007 and GA36-1992, combining the features of license plate characters, the license characters are divided into four classes, and trained respectively for a better recognition rate.All algorithms proposed in this paper have been simulated by MATLAB 7.0, and programmed with C. The results show that the accuracy rate of location algorithm for the gate scenes and the crossroads is 97.5% and 95.9%, the accuracy rate of character recognition is 97.65% and 91.48%.