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Towards Human-Level License Plate Recognition

Part of the Lecture Notes in Computer Science book series (LNIP,volume 11207)

Abstract

License plate recognition (LPR) is a fundamental component of various intelligent transport systems, which is always expected to be accurate and efficient enough. In this paper, we propose a novel LPR framework consisting of semantic segmentation and character counting, towards achieving human-level performance. Benefiting from innovative structure, our method can recognize a whole license plate once rather than conducting character detection or sliding window followed by per-character recognition. Moreover, our method can achieve higher recognition accuracy due to more effectively exploiting global information and avoiding sensitive character detection, and is time-saving due to eliminating one-by-one character recognition. Finally, we experimentally verify the effectiveness of the proposed method on two public datasets (AOLP and Media Lab) and our License Plate Dataset. The results demonstrate our method significantly outperforms the previous state-of-the-art methods, and achieves the accuracies of more than 99% for almost all settings.

Keywords

  • License Plate Recognition (LPR)
  • Semantic segmentation
  • Convolutional Neural Networks (CNN)
  • Character counting

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Notes

  1. 1.

    In the current version of our dataset, \(m=3\) is used due to the limitation of image acquisition.

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Acknowledgment

This work is supported partially by the NSFC under Grant 61673362, Youth Innovation Promotion Association CAS, and the Fundamental Research Funds for the Central Universities.

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Correspondence to Zilei Wang .

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Zhuang, J., Hou, S., Wang, Z., Zha, ZJ. (2018). Towards Human-Level License Plate Recognition. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds) Computer Vision – ECCV 2018. ECCV 2018. Lecture Notes in Computer Science(), vol 11207. Springer, Cham. https://doi.org/10.1007/978-3-030-01219-9_19

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  • DOI: https://doi.org/10.1007/978-3-030-01219-9_19

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