Robust Model for Chinese License Plate Character Recognition Using Deep Learning Techniques

Abstract

Character recognition and classification is considered one of the most important parts of current LPR systems. Because of low recognition quality and poor robustness of traditional character recognition techniques, those techniques were gradually replaced by powerful deep learning modules such as convolutional neural networks. Convolutional neural networks (CNNs) show satisfying ability in character recognition and outperform most of other available models. Since Chinese license plates contain Chinese characters in addition to ordinary alphanumeric characters, a robust, powerful, and efficient CNN is needed to accomplish character recognition task efficiently. In this paper, we have proposed an efficient CNN model based on Darknet architecture to perform character recognition. Through convolutional and max pooling layers, features of input character images will be extracted and then sent to softmax layer for classification. To avoid overfitting problem in the training process, the dropout regularization technique is adopted. We have used a dataset of 84,000 character images for training and testing our model. The experimental result shows satisfactory outputs and eventually achieves test accuracy of 99.69%.