Abstract
The multi-type license plate can be roughly classified into two categories, i.e., one-line and two-line. Many previous methods are proposed for horizontal one-line license plate recognition and consider license plate recognition as a one-dimensional sequence recognition problem. However, for multidirectional and two-line license plates, the features of adjacent characters may mix together when directly transforming a license plate image into a one-dimensional feature sequence. To solve this problem, we propose a two-dimensional spatial attention module to recognize license plates from a two-dimensional perspective. Specifically, we devise a lightweight and effective network for multidirectional and multi-type license plate recognition in the wild. The proposed network can work in parallel with a fast running speed because it does not contain any time-consuming recurrent structures. Extensive experiments on both public and private datasets verify that the proposed method outperforms state-of-the-art methods and achieves a real-time speed of 278 FPS. Our codes are available at https://github.com/qiLiu77/SALPR.
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The research is supported by National Natural Science Foundation of China (62006018).
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Liu, Q., Chen, SL., Li, ZJ., Yang, C., Chen, F., Yin, XC. (2021). Fast Recognition for Multidirectional and Multi-type License Plates with 2D Spatial Attention. In: Lladós, J., Lopresti, D., Uchida, S. (eds) Document Analysis and Recognition – ICDAR 2021. ICDAR 2021. Lecture Notes in Computer Science(), vol 12824. Springer, Cham. https://doi.org/10.1007/978-3-030-86337-1_9
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