License Plate Segmentation Method Using Deep Learning Techniques

Abstract

This paper proposes a new method for segmenting Chinese license plates in which the license plate segmentation is performed using powerful deep learning techniques instead of traditional digital image processing techniques. Firstly, input license plate image is preprocessed using traditional digital image processing techniques: input image is converted into gray scale, and then skew detection and correction is performed. Secondly, license plate is segmented and characters are separated using a well-trained convolutional neural network (CNN) so that each character is in its own image. Those characters can be later recognized and classified using any character recognition module. The proposed license plate segmentation method is straight forward, less complex, and can be considered as a good alternative for some traditional digital image processing license plate segmentation methods. Also the main concept of proposed segmentation method has good extensibility so that it can be extended to any kind and format of license plates easily.