Skip to main content
Book cover

European Conference on Computer Vision

ECCV 2018: Computer Vision – ECCV 2018 pp 314–329Cite as

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

This is a preview of subscription content, access via your institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-030-01219-9_19
  • Chapter length: 16 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   99.00
Price excludes VAT (USA)
  • ISBN: 978-3-030-01219-9
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   129.99
Price excludes VAT (USA)
Learn about institutional subscriptions
Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.
Fig. 5.
Fig. 6.
Fig. 7.
Fig. 8.

Notes

  1. 1.

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

References

  1. Adorni, G., Bergenti, F., Cagnoni, S.: Vehicle license plate recognition by means of cellular automata. In: IV (1998)

    Google Scholar 

  2. Anagnostopoulos, C.N.E., Anagnostopoulos, I.E., Loumos, V., Kayafas, E.: A license plate-recognition algorithm for intelligent transportation system applications. IEEE Trans. Intell. Transp. Syst. 7(3), 377–392 (2006)

    CrossRef  Google Scholar 

  3. Anagnostopoulos, C.N.E., Anagnostopoulos, I.E., Psoroulas, I.D., Loumos, V., Kayafas, E.: License plate recognition from still images and video sequences: a survey. IEEE Trans. Intell. Transp. Syst. 9(3), 377–391 (2008)

    CrossRef  Google Scholar 

  4. Arteta, C., Lempitsky, V., Noble, J.A., Zisserman, A.: Interactive object counting. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8691, pp. 504–518. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10578-9_33

    CrossRef  Google Scholar 

  5. Badrinarayanan, V., Kendall, A., Cipolla, R.: SegNet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39(12), 2481–2495 (2017)

    CrossRef  Google Scholar 

  6. Bulan, O., Kozitsky, V., Ramesh, P., Shreve, M.: Segmentation-and annotation-free license plate recognition with deep localization and failure identification. IEEE Trans. Intell. Transp. Syst. 18(9), 2351–2363 (2017)

    CrossRef  Google Scholar 

  7. Cheang, T.K., Chong, Y.S., Tay, Y.H.: Segmentation-free vehicle license plate recognition using convnet-RNN. arXiv preprint arXiv:1701.06439 (2017)

  8. Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Trans. Pattern Anal. Mach. Intell. 40(4), 834–848 (2018)

    CrossRef  Google Scholar 

  9. Cherng, S., Fang, C.Y., Chen, C.P., Chen, S.W.: Critical motion detection of nearby moving vehicles in a vision-based driver-assistance system. IEEE Tran. Intell. Transp. Syst. 10(1), 70–82 (2009)

    CrossRef  Google Scholar 

  10. Davies, P., Emmott, N., Ayland, N.: License plate recognition technology for toll violation enforcement. In: Image Analysis for Transport Applications (1990)

    Google Scholar 

  11. Du, S., Ibrahim, M., Shehata, M., Badawy, W.: Automatic license plate recognition (ALPR): a state-of-the-art review. IEEE Trans. Circuits Syst. Video Technol. 23(2), 311–325 (2013)

    CrossRef  Google Scholar 

  12. Duan, T.D., Du, T.H., Phuoc, T.V., Hoang, N.V.: Building an automatic vehicle license plate recognition system. In: RIVF (2005)

    Google Scholar 

  13. Hegt, H.A., De La Haye, R.J., Khan, N.A.: A high performance license plate recognition system (1998)

    Google Scholar 

  14. Hsu, G.S., Alexandra, P., Chen, J.C., Yeh, F., Chen, M.H.: License plate recognition for categorized applications. In: ICVES (2011)

    Google Scholar 

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

    CrossRef  Google Scholar 

  16. Huang, Y.S., Weng, Y.S., Zhou, M.: Critical scenarios and their identification in parallel railroad level crossing traffic control systems. IEEE Trans. Intell. Transp. Syst. 11(4), 968–977 (2010)

    CrossRef  Google Scholar 

  17. Jia, Y., et al.: Caffe: convolutional architecture for fast feature embedding. In: ACM MM (2014)

    Google Scholar 

  18. Kim, K.K., Kim, K., Kim, J., Kim, H.J.: Learning-based approach for license plate recognition. In: Neural Networks for Signal Processing (2000)

    Google Scholar 

  19. Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: NIPS (2012)

    Google Scholar 

  20. Lempitsky, V., Zisserman, A.: Learning to count objects in images. In: NIPS (2010)

    Google Scholar 

  21. Li, H., Shen, C.: Reading car license plates using deep convolutional neural networks and LSTMs. arXiv preprint arXiv:1601.05610 (2016)

  22. 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

    CrossRef  Google Scholar 

  23. Liu, X., Wang, Z., Feng, J., Xi, H.: Highway vehicle counting in compressed domain. In: CVPR (2016)

    Google Scholar 

  24. Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: CVPR (2015)

    Google Scholar 

  25. Masood, S.Z., Shu, G., Dehghan, A., Ortiz, E.G.: License plate detection and recognition using deeply learned convolutional neural networks. arXiv preprint arXiv:1703.07330 (2017)

  26. Naito, T., Tsukada, T., Yamada, K., Kozuka, K., Yamamoto, S.: Robust license-plate recognition method for passing vehicles under outside environment. IEEE Trans. Veh. Technol. 49(6), 2309–2319 (2000)

    CrossRef  Google Scholar 

  27. Nijhuis, J., et al.: Car license plate recognition with neural networks and fuzzy logic (1995)

    Google Scholar 

  28. Omitaomu, O.A., Ganguly, A.R., Patton, B.W., Protopopescu, V.A.: Anomaly detection in radiation sensor data with application to transportation security. IEEE Trans. Intell. Transp. Syst. 10(2), 324–334 (2009)

    CrossRef  Google Scholar 

  29. Parizi, S.N., Targhi, A.T., Aghazadeh, O., Eklundh, J.O.: Reading street signs using a generic structured object detection and signature recognition approach. In: VISAPP (2009)

    Google Scholar 

  30. Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: CVPR (2016)

    Google Scholar 

  31. Wang, F., Man, L., Wang, B., Xiao, Y., Pan, W., Lu, X.: Fuzzy-based algorithm for color recognition of license plates. Pattern Recogn. Lett. 29(7), 1007–1020 (2008)

    CrossRef  Google Scholar 

  32. Wu, Y., Li, J.: License plate recognition using deep FCN. In: Sun, F., Liu, H., Hu, D. (eds.) ICCSIP 2016. CCIS, vol. 710, pp. 225–234. Springer, Singapore (2017). https://doi.org/10.1007/978-981-10-5230-9_25

    CrossRef  Google Scholar 

  33. Yamaguchi, K., Nagaya, Y., Ueda, K., Nemoto, H., Nakagawa, M.: A method for identifying specific vehicles using template matching (1999)

    Google Scholar 

  34. Yu, M., Kim, Y.D.: An approach to Korean license plate recognition based on vertical edge matching (2000)

    Google Scholar 

  35. Zhu, S., Dianat, S., Mestha, L.K.: End-to-end system of license plate localization and recognition. JEI 24(2), 023020 (2015)

    Google Scholar 

Download references

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.

Author information

Authors and Affiliations

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

    Jiafan Zhuang, Saihui Hou, Zilei Wang & Zheng-Jun Zha

Authors
  1. Jiafan Zhuang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Saihui Hou
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zilei Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Zheng-Jun Zha
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zilei Wang .

Editor information

Editors and Affiliations

  1. Google Research, Zurich, Switzerland

    Vittorio Ferrari

  2. Carnegie Mellon University, Pittsburgh, PA, USA

    Prof. Martial Hebert

  3. Google Research, Zurich, Switzerland

    Cristian Sminchisescu

  4. Hebrew University of Jerusalem, Jerusalem, Israel

    Yair Weiss

Rights and permissions

Reprints and Permissions

Copyright information

© 2018 Springer Nature Switzerland AG

About this paper

Verify currency and authenticity via CrossMark

Cite this paper

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

Download citation

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

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-01218-2

  • Online ISBN: 978-3-030-01219-9

  • eBook Packages: Computer ScienceComputer Science (R0)