In this paper, we propose a real-time and accurate automatic license plate
recognition (ALPR) approach. Our study illustrates the outstanding design of
ALPR with four insights: (1) the resampling-based cascaded framework is
beneficial to both speed and accuracy; (2) the highly efficient license plate
recognition should abundant additional character segmentation and recurrent
neural network (RNN), but adopt a plain convolutional neural network (CNN); (3)
in the case of CNN, taking advantage of vertex information on license plates
improves the recognition performance; and (4) the weight-sharing character
classifier addresses the lack of training images in small-scale datasets. Based
on these insights, we propose a novel ALPR approach, termed VSNet.
Specifically, VSNet includes two CNNs, i.e., VertexNet for license plate
detection and SCR-Net for license plate recognition, integrated in a
resampling-based cascaded manner. In VertexNet, we propose an efficient
integration block to extract the spatial features of license plates. With
vertex supervisory information, we propose a vertex-estimation branch in
VertexNet such that license plates can be rectified as the input images of
SCR-Net. In SCR-Net, we introduce a horizontal encoding technique for
left-to-right feature extraction and propose a weight-sharing classifier for
character recognition. Experimental results show that the proposed VSNet
outperforms state-of-the-art methods by more than 50% relative improvement on
error rate, achieving > 99% recognition accuracy on CCPD and AOLP datasets with
149 FPS inference speed. Moreover, our method illustrates an outstanding
generalization capability when evaluated on the unseen PKUData and CLPD
datasets.

本文提出了一种实时精准的自动车牌识别方法，其中采用了基于卷积神经网络的车牌识别模型、几何校正模型以及字符编码器等技术，通过使用顶点信息和共享特征分类器等，达到了较好的识别效果及较强的泛化能力。