Skip to main content
SpringerLink
Log in

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

  • Conference paper
  • First Online:
Book cover Intelligent Computing Methodologies (ICIC 2022)

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

Included in the following conference series:

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 229.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 299.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Shashirangana, J., et al.: Automated license plate recognition: a survey on methods and techniques. IEEE Access 9, 11203–11225 (2021)

    Article  Google Scholar 

  2. Wang, W., et al.: A light CNN for end-to-end car license plates detection and recognition. IEEE Access 7, 173875–173883 (2019)

    Article  Google Scholar 

  3. Hoang, V.-T., et al.: 3D facial landmarks detection for intelligent video systems. IEEE Trans. Industr. Inf. 17(1), 578–586 (2021)

    Article  Google Scholar 

  4. Shen, Z., et al.: A deep learning model for RNA-protein binding preference prediction based on hierarchical LSTM and attention network. In: IEEE/ACM Transactions on Computational Biology and Bioinformatics, p. 1 (2020)

    Google Scholar 

  5. Wu, Y., et al.: Person Re-identification by Multi-scale Feature Representation Learning with Random Batch Feature Mask. IEEE Trans. Cogn. Dev. Syst. 13(4), 865–874 (2020)

    Article  Google Scholar 

  6. Wu, D., et al.: Attention deep model with multi-scale deep supervision for person re-identification. IEEE Trans. Emerging Topics Comput. Intell. 5(1), 70–78 (2021)

    Article  Google Scholar 

  7. Wu, D., et al.: Deep learning-based methods for person re-identification: a comprehensive review. Neurocomputing 337, 354–371 (2019)

    Article  Google Scholar 

  8. Wu, D., et al.: A novel deep model with multi-loss and efficient training for person re-identification. Neurocomputing 513, 662–674 (2019)

    Google Scholar 

  9. Wang, H., et al.: Robust Korean license plate recognition based on deep neural networks. Sensors (Basel) 21(12), 4140 (2021)

    Article  Google Scholar 

  10. Huang, D.S.: Radial basis probabilistic neural networks: model and application. Int. J. Pattern Recogn. Artif. Intell. 13(7), 1083–1101 (1999)

    Article  Google Scholar 

  11. LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)

    Article  Google Scholar 

  12. Li, Z., et al.: License Plate Detection and Recognition Technology for Complex Real Scenarios. In: Huang, D.-S., Bevilacqua, V., Hussain, A. (eds.) ICIC 2020. LNCS, vol. 12463, pp. 241–256. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-60799-9_21

    Chapter  Google Scholar 

  13. Selmi, Z., et al.: DELP-DAR system for license plate detection and recognition. Pattern Recogn. Lett. 129, 213–223 (2020)

    Article  Google Scholar 

  14. Zherzdev, S., Gruzdev, A.: LPRNet: license plate recognition via deep neural networks. arXiv:1806.10447 (2018)

  15. Zhang, M., et al., Chinese license plates recognition method based on a robust and efficient feature extraction and BPNN algorithm. In: Journal of Physics: Conference Series, vol. 1004 (2018)

    Google Scholar 

  16. Xu, Z., et al.: Towards end-to-end license plate detection and recognition: a large dataset and baseline. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11217, pp. 261–277. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01261-8_16

    Chapter  Google Scholar 

  17. Yang, Y., Li, D., Duan, Z.: Chinese vehicle license plate recognition using kernel-based extreme learning machine with deep convolutional features. IET Intel. Transport Syst. 12(3), 213–219 (2018)

    Article  Google Scholar 

  18. Zhen-Xue, C., et al.: Automatic license-plate location and recognition based on feature salience. IEEE Trans. Veh. Technol. 58(7), 3781–3785 (2009)

    Article  Google Scholar 

  19. Li, H., et al.: Reading car license plates using deep neural networks. Image Vis. Comput. 72, 14–23 (2018)

    Article  Google Scholar 

  20. Shi, B., Bai, X., Yao, C.: An end-to-end trainable neural network for image-based sequence recognition and its application to scene text recognition. IEEE Trans. Pattern Anal. Mach. Intell. 39(11), 2298–2304 (2017)

    Article  Google Scholar 

  21. Jaderberg, M., et al.: Spatial transformer networks. In: Advances in Neural Information Processing Systems (NIPS 2015), vol. 28 (2015)

    Google Scholar 

  22. Silva, S.M., Jung, C.R.: Real-time license plate detection and recognition using deep convolutional neural networks. J. Vis. Commun. Image Represent. 71, 102773 (2020)

    Article  Google Scholar 

  23. Selmi, Z., Ben Halima, M., Alimi, A.M.: Deep learning system for automatic license plate detection and recognition. In: 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), pp. 1132–1138 (2017)

    Google Scholar 

  24. Liang, X., Wu, D., Huang, D.-S.: Image co-segmentation via locally biased discriminative clustering. IEEE Trans. Knowl. Data Eng. 31(11), 2228–2233 (2019)

    Article  Google Scholar 

  25. Lianga, X., Zhua, L., Huang, D.-S.: Multi-task ranking SVM for image cosegmentaiton. Neurocomputing 247, 126–136 (2017)

    Article  Google Scholar 

  26. Hendry, Chen, R.-C.: Automatic license plate recognition via sliding-window darknet-YOLO deep learning. Image Vis. Comput. 87, 47–56 (2019)

    Google Scholar 

  27. Hsu, G.-S., Chen, J.-C., Chung, Y.-Z.: Application-oriented license plate recognition. IEEE Trans. Veh. Technol. 62(2), 552–561 (2013)

    Article  Google Scholar 

  28. Chen, S.-L., et al.: Simultaneous end-to-end vehicle and license plate detection with multi-branch attention neural network. IEEE Trans. Intell. Transp. Syst. 21(9), 3686–3695 (2020)

    Article  Google Scholar 

  29. Xie, L., et al.: A new CNN-based method for multi-directional car license plate detection. IEEE Trans. Intell. Transp. Syst. 19(2), 507–517 (2018)

    Article  Google Scholar 

  30. Lu, Q., Liu, Y., Huang, J., Yuan, X., Hu, Q.: License plate detection and recognition using hierarchical feature layers from CNN. Multimedia Tools Appl. 78(11), 15665–15680 (2018). https://doi.org/10.1007/s11042-018-6889-1

    Article  Google Scholar 

  31. Wang, Q.: License plate recognition via convolutional neural networks. In: 2017 8th IEEE International Conference on Software Engineering and Service Science (ICSESS), Beijing, China, pp. 926–929. IEEE (2017)

    Google Scholar 

  32. Liu, W., et al.: SSD: single shot MultiBox detector. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 21–37. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46448-0_2

    Chapter  Google Scholar 

  33. Girshick, R., et al.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA. pp. 580–587. IEEE (2014)

    Google Scholar 

  34. Girshick, R.: Fast_R-CNN__2015_paper. In: ICCV, pp. 1440–1448. IEEE (2015)

    Google Scholar 

  35. Redmon, J., et al.: You only look once: unified, real-time object detection. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 779–788 (2016)

    Google Scholar 

  36. Huang, Q., Cai, Z., Lan, T.: A single neural network for mixed style license plate detection and recognition. IEEE Access 9, 21777–21785 (2021)

    Article  Google Scholar 

  37. Tian, Z., et al.: FCOS: fully convolutional one-stage object detection. In: 2019 IEEE/CVF International Conference on Computer Vision (ICCV), pp. 9626–9635 (2019)

    Google Scholar 

  38. Tao, T., et al.: Object detection-based license plate localization and recognition in complex environments. Transp. Res. Record: J. Transp. Res. Board 2674(12), 212–223 (2020)

    Article  Google Scholar 

  39. Zhuang, J., Hou, S., Wang, Z., Zha, Z.-J.: Towards human-level license plate recognition. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11207, pp. 314–329. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01219-9_19

    Chapter  Google Scholar 

  40. Laroca, R., et al.: An efficient and layout-independent automatic license plate recognition system based on the YOLO detector. IET Intel. Transport Syst. 15(4), 483–503 (2021)

    Article  Google Scholar 

  41. Laroca, R., et al.: A robust real-time automatic license plate recognition based on the YOLO detector. In: 2018 International Joint Conference on Neural Networks (IJCNN). IEEE: Rio de Janeiro, Brazil (2018)

    Google Scholar 

  42. Kessentini, Y., et al.: A two-stage deep neural network for multi-norm license plate detection and recognition. Expert Syst. Appl. 136, 159–170 (2019)

    Article  Google Scholar 

  43. Online: A. (2021). https://github.com/ultralytics/yolov5

  44. Shu, X., Tang, J.: Hierarchical long short-term concurrent memory for human interaction recognition. IEEE Trans. Pattern Anal. Mach. Intell. 43(3), 1110–1118 (2021)

    Article  Google Scholar 

  45. Huang, Z., Xu, W., Yu, K.: Bidirectional LSTM-CRF models for sequence tagging. arXiv 2015, arxiv.org/1508.01991 (2015)

    Google Scholar 

  46. Henry, C., Ahn, S.Y., Lee, S.-W.: Multinational license plate recognition using generalized character sequence detection. IEEE Access 8, 35185–35199 (2020)

    Article  Google Scholar 

  47. Omar, N., Sengur, A., Al-Ali, S.G.S.: Cascaded deep learning-based efficient approach for license plate detection and recognition. Expert Syst. Appl. 149, 113280 (2020)

    Article  Google Scholar 

  48. Tourani, A., et al.: A Robust deep learning approach for automatic Iranian vehicle license plate detection and recognition for surveillance systems. IEEE Access 8, 201317–201330 (2020)

    Article  Google Scholar 

Download references

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
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhipeng Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Changan Yuan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hongjie Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Xingming Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Fahd A. Ghanem
    View author publications

    You can also search for this author in PubMed Google Scholar

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

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

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

Download citation

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

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-13831-7

  • Online ISBN: 978-3-031-13832-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation