Affiliations: [a] School of Information Engineering, Mianyang Teachers’ College, Mianyang, Sichuan, China | [b] Education and Information Technology Center, China West Normal University, Nanchong, Sichuan, China
Correspondence:
[*]
Corresponding author: Chun He, Education and Information Technology Center, China West Normal University, Nanchong, Sichuan, China. E-mail: [email protected].
Abstract: License plate location is one of the main research topics in intelligent traffic management. The whole process of license plate location and recognition is as follows: preprocessing the image and get the location of the license plate roughly by combining morphology; screening the license plates that match the features; obtaining the location of the license plate accurately, and finally completing the character recognition. The traditional license plate location methods based on edge, color, texture and machine learning need to extract complex features to license plate image, which can not only lead to overfitting or dimensionality in the training process, but also be affected by illumination, road environment and image quality etc. This paper proposed an algorithm of vehicle license plate location based on convolutional neural network, which avoids the problem that the traditional location algorithms have to preprocess images and need rich experience to extract the feature of the samples. The experimental results show that the convolutional neural network has better performance in license plate location compared with BP neural network and support vector machine (SVM), and also prove that deep learning has wide application in the field of intelligent transportation.