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An Effective Method for Yemeni License Plate Recognition Based on Deep Neural Networks

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Intelligent Computing Methodologies (ICIC 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13395))

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Abstract

As the existing publicly license plate recognition (LPR) datasets available for training are restricted and almost non-existent in some countries, for example, some developing countries. In this paper, first we present the first Yemeni License Plate dataset (Y-LPR dataset) includes vehicles and license plate images for Yemeni license plate detection and recognition. Second, we propose a new LPR method for license plate detection and Recognition. It consists of two key stages: First, License plate detection from images based on the latest state-of-the-art deep learning-based detector which is YOLOv5. Second, Yemeni Character and number recognition based on the CRNN model. Experimental results show that our method is effective in detecting and recognizing license plates.

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Acknowledgements

This work was supported by the grant of National Key R&D Program of China (No. 2018AAA0100100 & 2018YFA0902600) and partly supported by National Natural Science Foundation of China (Grant nos. 61732012, 62002266, 61932008, and 62073231), and Introduction Plan of High-end Foreign Experts (Grant no. G2021033002L) and, respectively, supported by the Key Project of Science and Technology of Guangxi (Grant no. 2021AB20147), Guangxi Natural Science Foundation (Grant nos. 2021JJA170204 & 2021JJA170199) and Guangxi Science and Technology Base and Talents Special Project (Grant nos. 2021AC19354 & 2021AC19394).

Author information

Authors and Affiliations

  1. Institute of Machine Learning and Systems Biology, School of Electronics and Information Engineering, Tongji University, Shanghai, 201804, China

    Hamdan Taleb & Zhipeng Li

  2. Department of Information Technology, College of Engineering and Information Technology, Aljanad University for Science and Technology, Taiz, Yemen

    Hamdan Taleb

  3. Guangxi Academy of Science, Nanning, 530007, China

    Changan Yuan

  4. Guangxi Key Lab of Human-Machine Interaction and Intelligent Decision, Guangxi Academy Sciences, Guangxi, China

    Changan Yuan

  5. School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou, 215009, China

    Hongjie Wu

  6. Institute of Science and Technology for Brain Inspired Intelligence (ISTBI), Fudan University, Shanghai, 200433, China

    Xingming Zhao

  7. Department of Computer Science and Engineering, PES College of Engineering, University of Mysore, Mandya, India

    Fahd A. Ghanem

  8. Department of Computer Science, College of Education – Zabid, Hodeidah University, Hodeidah, Yemen

    Fahd A. Ghanem

Authors
  1. Hamdan Taleb
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  2. Zhipeng Li
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  3. Changan Yuan
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  4. Hongjie Wu
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  5. Xingming Zhao
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  6. Fahd A. Ghanem
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Corresponding author

Correspondence to Hamdan Taleb .

Editor information

Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. University of Ulsan, Ulsan, Korea (Republic of)

    Kang-Hyun Jo

  3. Xi’an Polytechnic University, Xi’an, China

    Junfeng Jing

  4. The University of Wollongong, North Wollongong, NSW, Australia

    Prashan Premaratne

  5. Polytecnic of Bari, Bari, Italy

    Vitoantonio Bevilacqua

  6. Liverpool John Moores University, Liverpool, UK

    Abir Hussain

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Taleb, H., Li, Z., Yuan, C., Wu, H., Zhao, X., Ghanem, F.A. (2022). An Effective Method for Yemeni License Plate Recognition Based on Deep Neural Networks. In: Huang, DS., Jo, KH., Jing, J., Premaratne, P., Bevilacqua, V., Hussain, A. (eds) Intelligent Computing Methodologies. ICIC 2022. Lecture Notes in Computer Science(), vol 13395. Springer, Cham. https://doi.org/10.1007/978-3-031-13832-4_26

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  • DOI: https://doi.org/10.1007/978-3-031-13832-4_26

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