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Efficient License Plate Recognition via Parallel Position-Aware Attention

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Pattern Recognition and Computer Vision (PRCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13536))

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Abstract

In recent years, Recurrent Neural Networks and Attention Mechanism are used for License Plate Recognition(LPR), and have achieved state-of-the-art performances. However, sequentially generating attention vectors for different characters might lead to attention drift problem and also degrade the recognition efficiency. To address these problems, we propose a novel parallel position-aware attention mechanism for high-performance LPR. Our new attention method focuses more on the positional features of each character in a parallel manner by modeling all characters independently. In order to alleviate the issue of unbalanced problem in existing LPR datasets, we generate a large-scale synthetic dataset via CycleGAN, which includes 500k license plate images and covers all regions in China. Experimental results on the synthesized and public datasets suggest that the proposed approach achieves excellent performance in terms of both accuracy and efficiency.

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Author information

Authors and Affiliations

  1. Anhui Provincial Key Laboratory of Multimodal Cognitive Computation, Hefei, China

    Wenzhong Wang, Chenglong Li & Jin Tang

  2. School of Computer Science and Technology, Anhui University, Hefei, China

    Tianxiang Wang, Wenzhong Wang & Jin Tang

Authors
  1. Tianxiang Wang
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  2. Wenzhong Wang
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  3. Chenglong Li
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  4. Jin Tang
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Corresponding author

Correspondence to Wenzhong Wang .

Editor information

Editors and Affiliations

  1. Southern University of Science and Technology, Shenzhen, China

    Shiqi Yu

  2. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Zhaoxiang Zhang

  3. Hong Kong Baptist University, Hong Kong, China

    Pong C. Yuen

  4. Northwestern Polytechnical University, Xi'an, China

    Junwei Han

  5. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Tieniu Tan

  6. Hong Kong Baptist University, Hong Kong, China

    Yike Guo

  7. Sun Yat-sen University, Guangzhou, China

    Jianhuang Lai

  8. Southern University of Science and Technology, Shenzhen, China

    Jianguo Zhang

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Wang, T., Wang, W., Li, C., Tang, J. (2022). Efficient License Plate Recognition via Parallel Position-Aware Attention. In: Yu, S., et al. Pattern Recognition and Computer Vision. PRCV 2022. Lecture Notes in Computer Science, vol 13536. Springer, Cham. https://doi.org/10.1007/978-3-031-18913-5_27

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  • DOI: https://doi.org/10.1007/978-3-031-18913-5_27

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-18912-8

  • Online ISBN: 978-3-031-18913-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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