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Irregular License Plate Recognition via Global Information Integration

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MultiMedia Modeling (MMM 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14555))

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Abstract

Irregular license plate recognition remains challenging due to the irregular layouts of characters, such as multi-line and perspective-distorted layouts. Many previous methods are based on different attention mechanisms, which generate attention weights and aggregate the features to obtain character features for recognition. However, we found that attention-based methods suffer from attention deviation and character misidentification of similar glyphs. We infer that the lack of global perception is the main reason for these problems. Hence, we propose to integrate sufficient global information into the network to improve irregular license plate recognition. Firstly, we propose the deformable spatial attention module to integrate global layout information into attention calculations, thus generating more accurate attention. Secondly, we propose the global perception module that integrates global visual information into feature extraction to enhance the completeness of character features, making them more representative, and thereby alleviating character misidentification. Experiments demonstrate that our method achieves state-of-the-art results, with a significant improvement of 6.7% on irregular license plates. Our codes are available at https://github.com/MMM2024/GP_LPR.

Y.-Y. Liu and Q. Liu—These authors contributed equally to this work.

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Acknowledgement

This work was partly supported by the National Key Research and Development Program of China under Grant 2020AAA0109700 and partly by the National Natural Science Foundation of China under Grant 62076024 and Grant 62006018.

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Authors and Affiliations

  1. School of Computer Science and Technology, University of Science and Technology Beijing, Beijing, China

    Yuan-Yuan Liu, Qi Liu, Song-Lu Chen & Xu-Cheng Yin

  2. EEasy Technology Company Ltd., Beijing, China

    Feng Chen

Authors
  1. Yuan-Yuan Liu
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  2. Qi Liu
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  3. Song-Lu Chen
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  4. Feng Chen
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  5. Xu-Cheng Yin
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Corresponding author

Correspondence to Xu-Cheng Yin .

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Editors and Affiliations

  1. University of Amsterdam, Amsterdam, The Netherlands

    Stevan Rudinac

  2. Delft University of Technology, Delft, The Netherlands

    Alan Hanjalic

  3. Delft University of Technology, Delft, The Netherlands

    Cynthia Liem

  4. University of Amsterdam, Amsterdam, The Netherlands

    Marcel Worring

  5. Reykjavik University, Reykjavik, Iceland

    Björn Þór Jónsson

  6. Microsoft Research Lab – Asia, Beijing, China

    Bei Liu

  7. The University of Tokyo, Tokyo, Japan

    Yoko Yamakata

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Liu, YY., Liu, Q., Chen, SL., Chen, F., Yin, XC. (2024). Irregular License Plate Recognition via Global Information Integration. In: Rudinac, S., et al. MultiMedia Modeling. MMM 2024. Lecture Notes in Computer Science, vol 14555. Springer, Cham. https://doi.org/10.1007/978-3-031-53308-2_24

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  • DOI: https://doi.org/10.1007/978-3-031-53308-2_24

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-53307-5

  • Online ISBN: 978-3-031-53308-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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