The Research On License Plate Recognition Technology Based On Support Vector Machine

Intelligent Transportation System(ITS)is the necessary approaches for transportation modernization.As a key technology in improving the intelligent traffic control,and an important part of ITS,License Plate Recognition(LPR)System has a lots of useful applications,such as intelligent management in Parking lots,road traffic surveillance and automatic charge in highway.Support Vector Machines(SVM)is a new method for pattern recognition lately.It operates on the principle of structure risk minimization,hence a better generalization ability is guaranteed.And Vehicles' License Plate Recognition(LPR)system is an important application of computer vision and pattern recognition in the intelligent transportation management,the improved accuracy of LPR is required.Combining them is taken as the start of this article.This text,we realize the license plate detection of license plate recognition system based on support vector machine and Convolutional Restricted Boltzmann Machine.The system uses the open source computer vision library OpenCV and achieves by image processing and machine learning algorithms.The specific process is to capture the foreground objects in video frames,and then to the prospect of a series of noise reduction,gray,two values,close operation find the contour,contour selection,image processing,and then the license plate candidate elected out of the SVM classifier,to get the real plate,and Using depth boltzmann model and license plate recognition.Specific process is through the detection and location of the license plate through a variety of image processing sequence to complete character segmentation,character feature extraction,optical character recognition(OCR)and other operations,the characters on the license plate of the analysis process,and then get the license plate characters.The system can achieve high recognition rate under normal circumstances.And is robust to illumination and other environmental,meet the requirements of the campus gate control system.