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Automatic license plate recognition (ALPR) is widely used in Intelligent Traffic System (ITS). Deep learning has become the mainstream technology for ALPR. At present, the most advanced ALPR technologies are all designed based on convolutional neural networks (CNNs). From 2020, the visual Transformer technology has been introduced into the computer vision (CV) filed, and has achieved excellent performance in many CV tasks. However, there are few researches on ALPR using visual transformer technology. In order to study the license plate detection and recognition using visual transformer, we evaluate the performance of two representative object detection visual Transformer methods including DETR and Deformable DETR on three license plate datasets. Experimental results show that the visual transformer methods can achieve good detection results, but their detection and recognition performance is worse than that of some SOTA object detection methods based on CNNs. In the future, with the emergence of new visual Transformer methods and the construction of some large-scale license plate datasets, visual transformer will achieve better performance of license plate detection and recognition.
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