Research On Method Of Quickly Recognizing Characters On Vehicle License Plate

With the rapid development of national economy, the quantity of automobile increases rapidly in our country. But vehicle management mode has not followed the step actually, even it is still controlled by manual management in some areas, so it is very important to improve the efficiency of traffic control. Along with the emergence and development of Intelligent Transportation System(ITS), all kinds of resources can be used more efficiently, and the intellectualized transportation system can be controlled in order. As an important part of ITS, License Plate Recognition(LPR) technology has been widely studied and applied. Given the current development status of the technology, how to recognize the plate character in greater accuracy and less time-consuming under complex background, and it has became a hotspot in pattern recognize research.LPR technology is mainly combined by some parts such as plate location, char split and char recognition etc. In this paper, based on the characteristics of the Chinese plate, we study some classical algorithms, then propose some more effective algorithms. Many experimental results indicated that compared with those existing algorithms, new method has been greatly improved in speed and accuracy.This paper studied LPR technology, the main innovation has three aspects as follows:1. Analyzed structural feature of common license plate, studied classical plate location algorithms, and proposed a method of license plate location based on multiple edge features in the end, it employed the method of color dimension reduction to minimize the redundant color information. Experiment showed that the plate area can be located quickly and accurately, and it also validated strong robustness.2. Based on3-value image obtained by plate location, we used method of Hough transform to rotate some license plates which were tilt, and used projection method to segment the string. The experiment results showed the good performance.3. Compared and analyzed those existing classical methods of character recognition, then presented the template matching algorithm based on sift feature extraction to recognize the Chinese character, proposed the method based on BP neural network to identify the letter and digit. The experimental performance showed that those methods has higher accuracy and velocity.