Non-Segmented Chinese License Plate Recognition Algorithm based on Deep neural Networks

Abstract

This paper studies the problem of Chinese license plate recognition (CLPR) in complex natural scenes. Aiming at the problem that the license plate is difficult to be segmented due to its tilt and distortion caused by the process of license plate photographing, an improved Convolutional Recurrent Neural Network (CRNN) deep neural network model for license plate recognition was proposed, which combined with Deep Convolutional Neural Networks (DCNN), Recurrent Neural Networks (RNN), Spatial Transformer Networks (STN) and Connectionist Temporal Classification (CTC) model. The recognition process does not need to be segmented, which avoids the error caused by plate segmentation and affects the recognition accuracy. The convolutional layers selects DenseNet, the latest CNN network architecture, to improve the feature extraction accuracy. The space conversion layers adds STN to affine the license plate, which improves the recognition accuracy. In order to evaluate the performance, Chinese City Parking Dataset (CCPD) is used to test, and the validity of the method is proved.