Reading car license plates using deep neural networks

Abstract

In this work, we tackle the problem of car license plate detection and recognition in natural scene images based on the powerful deep neural networks (DNNs). Firstly, a 37-class convolutional neural network (CNN) is trained to detect characters in an image, which leads to a high recall compared with a binary text/non-text classifier. False positives are then eliminated effectively by a plate/non-plate CNN classifier. As to the license plate recognition, we regard the character string reading as a sequence labeling problem. Recurrent neural networks (RNNs) with long short-term memory (LSTM) are trained to recognize the sequential features extracted from the whole license plate via CNNs. The main advantage of this approach is that it is segmentation free. By exploring contextual information and avoiding errors caused by segmentation, this method performs better than conventional methods and achieves state-of-the-art recognition accuracy.