本文提出了LPRNet – 自动车牌识别的端到端学习方法,没有预处理步骤的字符分割。我们的方法受深度神经网络最新突破的启发,并且可以实时工作,中文牌照识别精度高达95%:在硬件配置nVIDIA GeForce GTX 1080、英特尔酷睿i7-6700K情况下可以实现1.3毫秒识别一个车牌。LPRNet由轻量级的卷积神经网络组成,因此可以以端到端的方式进行训练。据我们所知,LPRNet是第一个不使用RNN的实时车牌识别系统。因此,LPRNet算法可用于为LPR创建嵌入式解决方案,即使在具有挑战性的中国车牌上也可获得高水平的精度。

This paper proposes LPRNet – end-to-end method for Automatic License Plate Recognition without preliminary character segmentation. Our approach is inspired by recent breakthroughs in Deep Neural Networks, and works in real-time with recognition accuracy up to 95% for Chinese license plates: 3 ms/plate on nVIDIA GeForce GTX 1080 and 1.3 ms/plate on Intel Core i7-6700K CPU. LPRNet consists of the lightweight Convolutional Neural Network, so it can be trained in end-to-end way. To the best of our knowledge, LPRNet is the first real-time License Plate Recognition system that does not use RNNs. As a result, the LPRNet algorithm may be used to create embedded solutions for LPR that feature high level accuracy even on challenging Chinese license plates.