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End-to-End Multi-line License Plate Recognition with Cascaded Perception

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Document Analysis and Recognition - ICDAR 2023 (ICDAR 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14191))

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Abstract

Due to the irregular layout, multi-line license plates are challenging to recognize, and previous methods cannot recognize them effectively and efficiently. In this work, we propose an end-to-end multi-line license plate recognition network, which cascades global type perception and parallel character perception to enhance recognition performance and inference speed. Specifically, we first utilize self-information mining to extract global features to perceive plate type and character layout, improving recognition performance. Then, we use the reading order to attend plate characters parallelly, strengthening inference speed. Finally, we propose extracting recognition features from shallow layers of the backbone to construct an end-to-end detection and recognition network. This way, it can reduce error accumulation and retain more plate information, such as character stroke and layout, to enhance recognition. Experiments on three datasets prove our method can achieve state-of-the-art recognition performance, and cross-dataset experiments on two datasets verify the generality of our method. Moreover, our method can achieve a breakneck inference speed of 104 FPS with a small backbone while outperforming most comparative methods in recognition.

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Acknowledgement

The research is supported by National Key Research and Development Program of China (2020AAA0109700), National Science Fund for Distinguished Young Scholars (62125601), and National Natural Science Foundation of China (62076024, 62006018, U22B2055).

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Authors and Affiliations

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

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

  2. EEasy Technology Company Ltd., Zhuhai, China

    Feng Chen

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

Correspondence to Xu-Cheng Yin .

Editor information

Editors and Affiliations

  1. TU Dortmund University, Dortmund, Germany

    Gernot A. Fink

  2. Adobe, College Park, MN, USA

    Rajiv Jain

  3. Osaka Metropolitan University, Osaka, Japan

    Koichi Kise

  4. Rochester Institute of Technology, Rochester, NY, USA

    Richard Zanibbi

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Chen, SL., Liu, Q., Chen, F., Yin, XC. (2023). End-to-End Multi-line License Plate Recognition with Cascaded Perception. In: Fink, G.A., Jain, R., Kise, K., Zanibbi, R. (eds) Document Analysis and Recognition - ICDAR 2023. ICDAR 2023. Lecture Notes in Computer Science, vol 14191. Springer, Cham. https://doi.org/10.1007/978-3-031-41734-4_17

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  • DOI: https://doi.org/10.1007/978-3-031-41734-4_17

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-41733-7

  • Online ISBN: 978-3-031-41734-4

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