Skip to main content
SpringerLink
Log in

Leveraging Model Fusion for Improved License Plate Recognition

  • Conference paper
  • First Online:
Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications (CIARP 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14470))

Included in the following conference series:

  • 137 Accesses

Abstract

License Plate Recognition (LPR) plays a critical role in various applications, such as toll collection, parking management, and traffic law enforcement. Although LPR has witnessed significant advancements through the development of deep learning, there has been a noticeable lack of studies exploring the potential improvements in results by fusing the outputs from multiple recognition models. This research aims to fill this gap by investigating the combination of up to 12 different models using straightforward approaches, such as selecting the most confident prediction or employing majority vote-based strategies. Our experiments encompass a wide range of datasets, revealing substantial benefits of fusion approaches in both intra- and cross-dataset setups. Essentially, fusing multiple models reduces considerably the likelihood of obtaining subpar performance on a particular dataset/scenario. We also found that combining models based on their speed is an appealing approach. Specifically, for applications where the recognition task can tolerate some additional time, though not excessively, an effective strategy is to combine 4–6 models. These models may not be the most accurate individually, but their fusion strikes an optimal balance between accuracy and speed.

Supported by the Coordination for the Improvement of Higher Education Personnel (CAPES) (# 88881.516265/2020-01), and by the National Council for Scientific and Technological Development (CNPq) (# 309953/2019-7 and # 308879/2020-1).

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 89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 119.99
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

Notes

  1. 1.

    https://github.com/AlexeyAB/darknet.

  2. 2.

    https://github.com/roatienza/deep-text-recognition-benchmark/.

  3. 3.

    Detailed information on which images were used to train, validate and test the models can be accessed at https://raysonlaroca.github.io/supp/lpr-model-fusion/.

  4. 4.

    To train the models, we excluded the few images from the ChineseLP dataset that are also found in CLPD (both datasets include internet-sourced images [20]).

References

  1. Atienza, R.: Vision transformer for fast and efficient scene text recognition. In: International Conference on Document Analysis and Recognition, pp. 319–334 (2021)

    Google Scholar 

  2. Baek, J., et al.: What is wrong with scene text recognition model comparisons? Dataset and model analysis. In: IEEE/CVF International Conference on Computer Vision (ICCV), pp. 4714–4722 (2019)

    Google Scholar 

  3. Bochkovskiy, A., Wang, C.Y., Liao, H.Y.M.: YOLOv4: optimal speed and accuracy of object detection, pp. 1–14. arXiv preprint arXiv:2004.10934 (2020)

  4. Borisyuk, F., Gordo, A., Sivakumar, V.: Rosetta: large scale system for text detection and recognition in images. In: ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 71–79 (2018)

    Google Scholar 

  5. Dlagnekov, L.: UCSD/Calit2 car license plate, make and model database (2005). https://www.belongielab.org/car_data.html

  6. Fan, X., Zhao, W.: Improving robustness of license plates automatic recognition in natural scenes. IEEE Trans. Intell. Transp. Syst. 23(10), 18845–18854 (2022)

    Google Scholar 

  7. Gao, Y., et al.: GroupPlate: toward multi-category license plate recognition. IEEE Trans. Intell. Transp. Syst. 24(5), 5586–5599 (2023)

    Google Scholar 

  8. Garcia-Bordils, S., et al.: Out-of-vocabulary challenge report. In: European Conference on Computer Vision, TiE: Text in Everything Workshop, pp. 1–17 (2022)

    Google Scholar 

  9. Gonçalves, G.R., Silva, S.P.G., Menotti, D., Schwartz, W.R.: Benchmark for license plate character segmentation. J. Electron. Imaging 25(5), 053034 (2016)

    Article  Google Scholar 

  10. Gonçalves, G.R., et al.: Real-time automatic license plate recognition through deep multi-task networks. In: Conference on Graphics, Patterns and Images (SIBGRAPI), pp. 110–117 (Oct 2018)

    Google Scholar 

  11. Gonçalves, G.R., et al.: Multi-task learning for low-resolution license plate recognition. In: Iberoamerican Congress on Pattern Recognition, pp. 251–261 (Oct 2019)

    Google Scholar 

  12. Gong, Y., et al.: Unified Chinese license plate detection and recognition with high efficiency. J. Vis. Commun. Image Represent. 86, 103541 (2022)

    Article  Google Scholar 

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

    Article  Google Scholar 

  14. Hsu, G.S., Ambikapathi, A., Chung, S.L., Su, C.P.: Robust license plate detection in the wild. In: IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017)

    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)

    Article  Google Scholar 

  16. Izidio, D.M.F., et al.: An embedded automatic license plate recognition system using deep learning. Des. Autom. Embed. Syst. 24(1), 23–43 (2020)

    Article  Google Scholar 

  17. Kabiraj, A., Pal, D., Ganguly, D., Chatterjee, K., Roy, S.: Number plate recognition from enhanced super-resolution using generative adversarial network. Multimedia Tools Appli. 82(9), 13837–13853 (2023)

    Article  Google Scholar 

  18. Ke, X., Zeng, G., Guo, W.: An ultra-fast automatic license plate recognition approach for unconstrained scenarios. IEEE Trans. Intell. Transp. Syst. 24(5), 5172–5185 (2023)

    Article  Google Scholar 

  19. Laroca, R., Cardoso, E.V., Lucio, D.R., Estevam, V., Menotti, D.: On the cross-dataset generalization in license plate recognition. In: International Conference on Computer Vision Theory and Applications (VISAPP), pp. 166–178 (2022)

    Google Scholar 

  20. Laroca, R., Estevam, V., Britto Jr., A.S., Minetto, R., Menotti, D.: Do we train on test data? the impact of near-duplicates on license plate recognition. In: International Joint Conference on Neural Networks (IJCNN), pp. 1–8 (2023)

    Google Scholar 

  21. Laroca, R., Severo, E., Zanlorensi, L.A., Oliveira, L.S., et al.: A robust real-time automatic license plate recognition based on the YOLO detector. In: International Joint Conference on Neural Networks (IJCNN), pp. 1–10 (2018)

    Google Scholar 

  22. Laroca, R., Zanlorensi, L., Gonçalves, G., Todt, E., Schwartz, W., Menotti, D.: 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 

  23. Laroca, R., et al.: Towards image-based automatic meter reading in unconstrained scenarios: a robust and efficient approach. IEEE Access 9, 67569–67584 (2021)

    Article  Google Scholar 

  24. Laroca, R., et al.: A first look at dataset bias in license plate recognition. In: Conference on Graphics, Patterns and Images (SIBGRAPI), pp. 234–239 (2022)

    Google Scholar 

  25. Lee, C., Osindero, S.: Recursive recurrent nets with attention modeling for OCR in the wild. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2231–2239 (2016)

    Google Scholar 

  26. Lee, Y., et al.: License plate detection via information maximization. IEEE Trans. Intell. Transp. Syst. 23(9), 14908–14921 (2022)

    Article  Google Scholar 

  27. Liu, Q., Chen, S.L., Li, Z.J., Yang, C., Chen, F., Yin, X.C.: Fast recognition for multidirectional and multi-type license plates with 2D spatial attention. In: International Conference on Document Analysis and Recognition (ICDAR), pp. 125–139 (2021)

    Google Scholar 

  28. Liu, W., Chen, C., Kwan-Yee K. Wong, Z.S., Han, J.: STAR-Net: a spatial attention residue network for scene text recognition. In: British Machine Vision Conference (BMVC), pp. 1–13 (Sept 2016)

    Google Scholar 

  29. Mokayed, H., Shivakumara, P., Woon, H.H., Kankanhalli, M., Lu, T., Pal, U.: A new DCT-PCM method for license plate number detection in drone images. Pattern Recogn. Lett. 148, 45–53 (2021)

    Article  Google Scholar 

  30. Nascimento, V., et al.: Super-resolution of license plate images using attention modules and sub-pixel convolution layers. Comput. Graph. 113, 69–76 (2023)

    Article  Google Scholar 

  31. OpenALPR: OpenALPR-EU dataset (2016). https://github.com/openalpr/benchmarks/tree/master/endtoend/eu

  32. Polikar, R.: Ensemble learning. Ensemble Machine Learning: Methods and Applications, pp. 1–34. Springer, New York (2012). https://doi.org/10.1007/978-1-4419-9326-7

  33. Schirrmacher, F., Lorch, B., Maier, A., Riess, C.: Benchmarking probabilistic deep learning methods for license plate recognition. IEEE Trans. Intell. Transp. Syst. 24(9), 9203–9216 (2023)

    Article  Google Scholar 

  34. 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 

  35. Shi, B., Wang, X., Lyu, P., Yao, C., Bai, X.: Robust scene text recognition with automatic rectification. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4168–4176 (2016)

    Google Scholar 

  36. Silva, S.M., Jung, C.R.: License plate detection and recognition in unconstrained scenarios. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11216, pp. 593–609. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01258-8_36

    Chapter  Google Scholar 

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

    Google Scholar 

  38. Silva, S.M., Jung, C.R.: A flexible approach for automatic license plate recognition in unconstrained scenarios. IEEE Trans. Intell. Transp. Syst. 23(6), 5693–5703 (2022)

    Article  Google Scholar 

  39. Špaňhel, J., et al.: Holistic recognition of low quality license plates by CNN using track annotated data. In: IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017)

    Google Scholar 

  40. Srebrić, V.: EnglishLP database (2003). https://www.zemris.fer.hr/projects/LicensePlates/english/baza_slika.zip

  41. Terven, J., Cordova-Esparza, D.: A comprehensive review of YOLO: From YOLOv1 and beyond, pp. 1–33. arXiv preprint arXiv:2304.00501 (2023)

  42. Wang, J., Hu, X.: Gated recurrent convolution neural network for OCR. In: International Conference on Neural Information Processing Systems (NeurIPS), pp. 334–343 (2017)

    Google Scholar 

  43. Wang, Y., Bian, Z.P., Zhou, Y., Chau, L.P.: Rethinking and designing a high-performing automatic license plate recognition approach. IEEE Trans. Intell. Transp. Syst. 23(7), 8868–8880 (2022)

    Article  Google Scholar 

  44. Weber, M.: Caltech Cars (1999). https://data.caltech.edu/records/20084

  45. Yuan, Y., Zou, W., Zhao, Y., Wang, X., Hu, X., Komodakis, N.: A robust and efficient approach to license plate detection. IEEE Trans. Image Process. 26(3), 1102–1114 (2017)

    Article  MathSciNet  MATH  Google Scholar 

  46. Zeni, L.F., Jung, C.R.: Weakly supervised character detection for license plate recognition. In: Conference on Graphics, Patterns and Images, pp. 218–225 (2020)

    Google Scholar 

  47. Zhang, L., Wang, P., Li, H., Li, Z., Shen, C., Zhang, Y.: A robust attentional framework for license plate recognition in the wild. IEEE Trans. Intell. Transp. Syst. 22(11), 6967–6976 (2021)

    Article  Google Scholar 

  48. Zhang, M., Liu, W., Ma, H.: Joint license plate super-resolution and recognition in one multi-task GAN framework. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1443–1447 (2018)

    Google Scholar 

  49. Zhou, W., et al.: Principal visual word discovery for automatic license plate detection. IEEE Trans. Image Process. 21(9), 4269–4279 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  50. Zou, Y., et al.: A robust license plate recognition model based on Bi-LSTM. IEEE Access 8, 211630–211641 (2020)

    Article  Google Scholar 

Download references

Acknowledgments

We thank the support of NVIDIA Corporation with the donation of the Quadro RTX 8000 GPU used for this research.

Author information

Authors and Affiliations

  1. Federal University of Paraná, Curitiba, Brazil

    Rayson Laroca, Luiz A. Zanlorensi, Valter Estevam & David Menotti

  2. Federal Institute of Paraná, Irati, Brazil

    Valter Estevam

  3. Federal University of Technology-Paraná, Curitiba, Brazil

    Rodrigo Minetto

Authors
  1. Rayson Laroca
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Luiz A. Zanlorensi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Valter Estevam
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Rodrigo Minetto
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. David Menotti
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Rayson Laroca .

Editor information

Editors and Affiliations

  1. Polytechnic Institute of Coimbra, Coimbra Institute of Engineering, Coimbra, Portugal

    Verónica Vasconcelos

  2. Polytechnic Institute of Coimbra, Coimbra Institute of Engineering, Coimbra, Portugal

    Inês Domingues

  3. Polytechnic Institute of Coimbra, Coimbra Institute of Engineering, Coimbra, Portugal

    Simão Paredes

Rights and permissions

Reprints and permissions

Copyright information

© 2024 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Laroca, R., Zanlorensi, L.A., Estevam, V., Minetto, R., Menotti, D. (2024). Leveraging Model Fusion for Improved License Plate Recognition. In: Vasconcelos, V., Domingues, I., Paredes, S. (eds) Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2023. Lecture Notes in Computer Science, vol 14470. Springer, Cham. https://doi.org/10.1007/978-3-031-49249-5_5

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-49249-5_5

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-49248-8

  • Online ISBN: 978-3-031-49249-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Societies and partnerships

Search

Navigation