Design of License Plate Recognition System Based on Machine Learning

Abstract

Aiming at the practicability of license plate recognition, a robust license plate recognition system is designed. Firstly, Sobel-Color algorithm is proposed to locate license plate based on Sobel edge and color features, and combined with MSER algorithm, a reliable license plate location method is designed to obtain candidate license plate regions, and then the SVM algorithm is used to judge them. Finally, a license plate character segmentation algorithm is designed according to the license plate characteristics, which can segment the characters of the license plate correctly, and effectively remove the false characters of the edge of the license plate. According to the characteristics of the license plate characters, the LeNet-5 depth network model is improved, and then the improved LeNet-5 network is used to recognize the license plate characters. The normal condition test, harsh condition test and efficiency test of the license plate recognition system are carried out. The experimental results show that the method of license plate location and license plate judgment has high reliability, and the license plate character recognition has high accuracy. Therefore, the designed license plate recognition system has good robustness and practicability.