"车牌字符识别算法比对及优劣分析"

License plate recognition technology is an essential component of intelligent transportation systems, playing a crucial role in various applications such as illegal capture, toll collection, parking management, and real-time registration of vehicles passing through important locations. This paper conducts a systematic study of three character recognition algorithms - template matching, neural networks, and Support Vector Machine (SVM), based on the theory of license plate character recognition. Using MATLAB, the paper analyzes the advantages, disadvantages, and recognition rates of these methods in the character recognition process. The main work of the paper includes pre-processing of license plate images, such as denoising, enhancement, segmentation, and character extraction. Test data for template matching, neural networks, and SVM character recognition algorithms are established, and experiments are conducted to compare the recognition rates of these three methods. Additionally, MATLAB GUI interfaces are developed for the three algorithms. In conclusion, this paper provides a comprehensive comparison of template matching, neural networks, and SVM algorithms for license plate character recognition. The results of the study can be valuable for improving the accuracy and efficiency of license plate recognition systems in intelligent transportation applications. Key words: license plate recognition, template matching, neural networks, Support Vector Machine, recognition rate.