[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content
Springer Nature Link
Log in

Vietnam Vehicle Number Recognition Based on an Improved CRNN with Attention Mechanism

  • Published:
International Journal of Intelligent Transportation Systems Research Aims and scope Submit manuscript

Abstract

Applications for automatic license plate recognition (ALPR) are evaluated as an important technology for effective traffic control. In particular, the most used application today for license plate recognition is at highways, toll stations, agencies, parking, and schools, with the support of camera equipment that provides high accuracy. In this study, Convolutional Recurrent Neural Networks (CRNNs) are utilized for the license plate recognition task. We are developing an automatic license plate recognition system designed for Vietnamese license plates, intended for use in indoor parking facilities with fixed cameras. In this context, the problem presents three main steps: (1) using the YOLO model to detect vehicles in the given images, (2) using the WPOD-NET model to extract license plates, and (3) introducing a new method based on an improved Convolution Recurrent Neural Network (CRNN) with combination between connectionist temporal classification (CTC) and attention mechanism to recognize characters on license plates. Our CRNN model is jointly trained with both CTC and attention objective functions. Experimental results on a license plate database collected from an indoor parking area achieved a Word Error Rate (WER) of 0.014 in the optical character recognition (OCR) task. The experimental results demonstrate that the proposed model performs well for vehicle number plate recognition and can be applied to real-time applications.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Abbreviations

ALPR :

Automatic license plate recognition

BLSTM :

Bidirectional Long short-term memory

CNN :

Convolutional neural network

CRNN :

Convolutional recurrent neural network

CTC :

Connectionist temporal classification

FAN :

Focusing attention network

HOG :

Histogram of oriented gradients

IoU :

Intersection over union

KNN :

K-nearest neighbors

LBP :

Local binary pattern

LSTM :

Long short-term memory

OCR :

Optical character recognition

SGD :

Stochastic gradient descent

STN :

Spatial transformer networks

SVM :

Support vector machines

WER :

Word error rate

WPOD-Net :

Warped planar object detection network

YOLO :

You only look once

References

  1. Wen, Y., Lu, Y., Yan, J., Zhou, Z., von Deneen, K.M., Shi, P.: An algorithm for license plate recognition applied to intelligent transportation system. IEEE Trans. Intell. Transp. Syst. 12(3), 830–845 (2011)

    Article  Google Scholar 

  2. 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 (2012)

    Article  Google Scholar 

  3. Han, C.C., Hsieh, C.T., Chen, Y.N., Ho, G.F., Fan, K.C., Tsai, C.L.: License plate detection and recognition using a dual-camera module in a large space. In 2007 41st Annual IEEE International Carnahan Conference on Security Technology, pp. 307–312 (2007)

  4. Lubna, Mufti, N., Shah, S.A.A.: Automatic number plate recognition: A detailed survey of relevant algorithms. Sensors. 21(9), 3028 (2021)

    Article  Google Scholar 

  5. Yepez, J., Ko, S.B.: Improved license plate localisation algorithm based on morphological operations. IET Intel. Transport Syst. 12(6), 542–549 (2018)

    Article  Google Scholar 

  6. Khan, M.F., Mufti, N.: Comparison of various edge detection filters for ANPR. In Proceedings of the Sixth International Conference on Innovative Computing Technology (INTECH), Dublin, Ireland, pp. 306–309 (2016)

  7. Sferle, R.M., Moisi, E.V.: Automatic number plate recognition for a smart service auto. In Proceedings of the 15th International Conference on Engineering of Modern Electric Systems (EMES), Oradea, Romania, pp. 57–60 (2019)

  8. Arafat, M.Y., Khairuddin, A.S.M., Paramesran, R.: Connected component analysis integrated edge-based technique for automatic vehicular license plate recognition framework. IET Intel. Transport Syst. 14(7), 712–723 (2020)

    Article  Google Scholar 

  9. Silva, S.M., Jung, C.R.: License plate detection and recognition in unconstrained scenarios. In Proceedings of the European conference on computer vision (ECCV), pp. 580–596 (2018)

  10. Laroca, R., Severo, E., Zanlorensi, L.A., Oliveira, L.S., Gonçalves, G.R., Schwartz, W.R., Menotti, D.: A robust real-time automatic license plate recognition based on the YOLO detector. In Proceedings of the International Joint Conference on Neural Networks (IJCNN), Rio de Janeiro, pp. 1–10 (2018)

  11. Montazzolli, S., Jung, C.: Real-time brazilian license plate detection and recognition using deep convolutional neural networks. In Proceedings of the 30th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI), Niteroi, Brazil, pp. 55–62 (2017)

  12. Slimani, I., Zaarane, I., Hamdoun, A., A., Atouf, I.: Vehicle license plate localization and recognition system for intelligent transportation applications. In Proceedings of the 6th International Conference on Control, Decision and Information Technologies (CoDIT), Paris, France, pp. 1592–1597 (2019)

  13. Li, M., Sun, T., Liu, H.: Image recognition of steel plate based on an improved support vector machine. In 2018 IEEE International Conference on Information and Automation (ICIA), Wuyishan, China, pp. 1411–1415 (2018)

  14. Bhushan, B., Singh, S., Singla, R.: License plate recognition system using neural networks and multithresholding technique. Int. J. Comput. Appl. 84(5), 45–50 (2013)

    Google Scholar 

  15. Soon, C., Lin, C.K., Jeng, K.C., C. Y., Suandi, S.A.: Malaysian car number plate detection and recognition system. Aust. J. Basic Appl. Sci. 6(3), 49–59 (2012)

    Google Scholar 

  16. Montazzolli, S., Jung, C.: Real-time brazilian license plate detection and recognition using deep convolutional neural networks. In 2017 30th SIBGRAPI conference on graphics, patterns and images, pp. 55–62 (2017)

  17. Selmi, Z., Halima, M.B., 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)

  18. Špaňhel, J., Sochor, J., Juránek, R., Herout, A., Maršík, L., Zemčík, P.: Holistic recognition of low-quality license plates by CNN using track annotated data. In 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2017)

  19. Agbemenu, A.S., Yankey, J., Addo, E.O.: An automatic number plate recognition system using opencv and tesseract ocr engine. Int. J. Comput. Appl. 180(43), 1–5 (2018)

    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 (2016)

    Article  Google Scholar 

  21. Cheng, Z., Bai, F., Xu, Y., Zheng, G., Pu, S., Zhou, S.: Focusing attention: Towards accurate text recognition in natural images. In Proceedings of the IEEE international conference on computer vision, pp. 5076–5084 (2017)

  22. Shi, B., Yang, M., Wang, X., Lyu, P., Yao, C., Bai, X.: Aster: An attentional scene text recognizer with flexible rectification. IEEE Trans. Pattern Anal. Mach. Intell. 41(9), 2035–2048 (2018)

    Article  Google Scholar 

  23. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv Preprint arXiv:14091556 (2014)

  24. Dat, T.T., Dang, L.T.A., Sang, V.N.T., Thuy, L.N.L., Bao, P.T.: Convolutional recurrent neural network with attention for Vietnamese speech to text problem in the operating room. Int. J. Intell. Inf. Database Syst. 14(3), 294–314 (2021)

    Google Scholar 

  25. Dat, T.T., Dang, L.T.A., Truong, N.N., Vu, P.C.L.T., Sang, V.N.T., Vuong, P.T., Bao, P.T.: An improved CRNN for Vietnamese Identity Card Information Recognition. Comput. Syst. Sci. Eng. 40(2), 539–555 (2022)

    Article  Google Scholar 

  26. Tesseract Open Source, O.C.R., Engine: (2019). https://github.com/tesseract-ocr/tesseract. Accessed 15 Apr 2023

Download references

Acknowledgements

This work was partly supported by Saigon University.

Author information

Authors and Affiliations

  1. Information Science Faculty, Sai Gon University, Ho Chi Minh City, Vietnam

    Le Tran Anh Dang, Vong Duong Ngoc, Pham Cung Le Thien Vu, Nguyen Nhat Truong, Pham The Bao & Tan Dat Trinh

Authors
  1. Le Tran Anh Dang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Vong Duong Ngoc
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Pham Cung Le Thien Vu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Nguyen Nhat Truong
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Pham The Bao
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Tan Dat Trinh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Tan Dat Trinh.

Ethics declarations

Conflict of Interest

The authors declare that they have no conflict of interest.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic Supplementary Material

Below is the link to the electronic supplementary material.

Supplementary Material 1

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Dang, L.T.A., Ngoc, V.D., Thien Vu, P.C.L. et al. Vietnam Vehicle Number Recognition Based on an Improved CRNN with Attention Mechanism. Int. J. ITS Res. 22, 374–389 (2024). https://doi.org/10.1007/s13177-024-00402-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13177-024-00402-7

Keywords

Search

Navigation