基于SVM的车牌字符识别分析-计算机科学与工程专业论文.docxVIP

Abstract Nowadays, with the high-speed development of socio-economic, the amount of vehicles has increased dramatically. The Intelligent Transport System（ITS）has become the main instrument of solving the problems of highway traffic and the traffic control of cities. And the License Plate Recognition (LPR) plays a main role in the ITS, in which the accuracy and speed of character recognition are key indicators. In view of the great significance of LPR technology, many researchers turn to work on it and achieved some positive results. However, Many of the traditional ideas, which are based on the image processing technology, such as template matching method and artificial neural network method, have shown some limitations: the need of large amount of samples, poor performance of promotion capacity, and low accuracy of character recognition. It is difficult to put them into practical applications. The Statistical Learning Theory (SLT) is built on the case of small samples, it supplies a better theoretical frame to the research of Statistical Pattern Recognition under the circumstances of limited samples, and presents a new pattern recognition method──the Support Vector Machine (SVM).The SVM can overcome problems such as the local optimal solution, dimension disaster and other difficulties which trouble the traditional pattern recognition methods badly. It also shows good promotion capacity in the case of small samples. This thesis focuses on the application of Support Vector Machine in character recognition of license plate. Firstly, some basic knowledge of the Statistical Learning Theory and Support Vector Machine is introduced. The training algorithms of Support Vector Machine are also discussed in detail. Then, the methods of character feature are researched; the wavelet grid feature and projection feature of character are raised. Finally, a character recognition software based on SVMLight algorithm is programmed. This software gains over 95% accuracy w