Research And Implementation Of The Key Technologies For License Plate Recognition System

License Plate Recognition is one of the most critical research topics in the Intelligent Transportation System. It has a broad application prospects, such as road traffic monitoring, automated highway toll collection, parking management, and so on. With the development of economic and society, the quantity of vehicles is increasing, and the demand of vehicle safety management, traffic guidance and control is more and more obviously. Therefore, it has great social significance and pratical value to make a research in more stable, fast and effective license plate recognition system.License plate recognition technology consists of three key components, i.e. license plate location, character segmentation and character recognition. This article used image processing and artificial neural network technologies, proposed effective improvements, and used VC++6.0 platform programming of the license plat recognition system. It included contents as follows:A new algorithm of license plate location based on horizontal and vertical projection is proposed. Firstly, after pre-processing, the image was smoothed to remove noise. Secondly, the image was projected in the horizontal and vertical direction after thresholding of binary image, when we got the upper and lower edge of the license plate area location, we could intercept the image. This method is simple, fast, and easy to understand, it can effectively locate the license plate.This article proposed a new method of license plate character segmentation which based on prior knowledge of plate. The method useed the fixed features of our license plate. Firstly, the upper and lower boundary of license characters was obtained by scanning each line of the plate. Secondly, the left and right boundary of license characters was obtained by scanning each column. Experiment results show that this method performs fast, and can effectively deal with the uncleared characters that caused by wear and pollution phenomena.Based on the analysis of template matching and artificial neural network character recognition methods. The article proposed a method of improved packet BP neural network character recognition. The method divided the BP neural network into four sub-networks, and added the momentum factor when weight changing. In the traditional BP network, when learning rate is too big or small, the network is easy to divergence or slow convergence. The packet BP network can slove this problem. Experiment results demonstrate that the method can effectively improve the speed of BP network training and character recognition accuracy.