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Aiming at the problems of low recognition rate and slow recognition speed of Chinese characters in license plate, a license plate recognition network Lenet-5-L based on deep learning is proposed, This network divides license plate recognition into two stages, using OpenCV library function to preprocess license plate image, combining with vertical projection segmentation method to divide license plate into seven independent character images, so as to reduce character recognition feature dimension and improve character recognition rate and speed; using volume neural network to solve the problem of license plate character recognition, a license plate character recognition network LeNet-5-L is designed based on LeNet-L can effectively improve the recognition rate of the first Chinese character in the license plate. The experimental results show that the recognition accuracy of the network is higher than 99.97%,and the recognition time of a single license plate is only 0.83ms. This method effectively improves the recognition accuracy and recognition speed.