Skip to main content
Springer Nature Link
Log in

Marathon athletes number recognition model with compound deep neural network

  • Original Paper
  • Published:
Signal, Image and Video Processing Aims and scope Submit manuscript

Abstract

A large number of photos are taken for each athlete during a marathon competition, therefore, how to classify photos of specific athletes accurately and effectively has become the focus of attention. In this paper, we propose a compound deep neural network for marathon athletes number recognition to make classification more efficient and accurate. The proposed model is divided into three modules: image preprocessing module, text detection module, and text recognition module. Firstly, in the preprocessing module, we make use of the You Only Look Once version 3, and set the detection threshold and similarity threshold to reduce unnecessary detection. Secondly, we combine the efficient text detector Connectionist Text Proposal Network and the excellent text recognition general framework Convolutional Recurrent Neural Network (CRNN) to recognize the athletes number plates. Besides, to improve the accuracy of detection, we use transfer learning to fine-tune the CRNN. Finally, we design an effective tree filtering algorithm to avoid the interference caused by the text detection module. It can filter out invalid results, thereby improving the accuracy of the model. Our model is capable of performing classification on photos of marathon athletes with high precision. The model is feasible and effective, as indicated by the experiment results.

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
$34.99 /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 excludes VAT (USA)
Tax calculation will be finalised during checkout.

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
Fig. 11
Fig. 12

Similar content being viewed by others

References

  1. Felzenszwalb, P.F., Mcallester, D.A., Raman, D.: A discriminatively trained, multiscale, deformable part model. In: IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 1–8 (2008)

  2. Shivakumara, P., Phan, T.Q., Tan, C.L.: A Laplacian approach to multi-oriented text detection in video. IEEE Trans. Pattern Anal. Mach. Intell. 33(2), 412–419 (2010)

    Article  Google Scholar 

  3. Chen, X., Yuille, A.L.: Detecting and reading text in natural scenes. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 366–373 (2004)

  4. Chen, Z., Chang, F., Liu, C.: Chinese license plate recognition based on human vision attention mechanism. Int. J. Pattern Recognit. Artif. Intell. 27(08), 1350024 (2013)

    Article  Google Scholar 

  5. Norizam, S., Mahfuzah, M., Kamarul Hawari, G.: Development of automatic vehicle plate detection system. In: IEEE International Conference on System Engineering and Technology (2013)

  6. Romanov, M., Miller, M. T., Savant, S.B., et al.: Important new developments in arabographic optical character recognition (OCR) (2017)

  7. Jinmei, L., Qiang, Q.U.: Randomness tests of several chaotic sequences. Comput. Eng. Appl. 47(5), 46–49 (2011)

    Google Scholar 

  8. Xucheng, Y., Weiyi, P., Jun, Z., Hongwei, H.: Multi-orientation scene text detection with adaptive clustering. IEEE Trans. Pattern Anal. Mach. Intell. 37(9), 19–30 (2015)

    Google Scholar 

  9. Tian, Z., Huang, W., He ,T., et al.: Detecting text in natural image with connectionist text proposal network. In: European Conference on Computer Vision (2016)

  10. Liao, M., Shi, B., Bai, X., et al.: TextBoxes: a fast text detector with a single deep neural network (2016)

  11. Liu, W., Anguelov, D., Erhan, D., et al.: SSD: Single Shot MultiBox Detector. In: European Conference on Computer Vision (2016)

  12. Krizhevsky, A., Sutskever, I., Hinton, G.: ImageNet classification with deep convolutional neural networks. In: International Conference on Neural Information Processing Systems (2012)

  13. Redmon, J., Farhadi, A.: YOLOv3: an incremental improvement (2018)

  14. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. Comput. Sci. (2014)

  15. Ren, S., He, K., Girshick, R.B., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39(6), 1137–1149 (2015)

    Article  Google Scholar 

  16. Redmon, J., Divvala, S., Girshick, R., et al.: You only look once: unified, real-time object detection (2015)

  17. 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 

  18. Graves, A., Santiago Fernández, G.F.: Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks. In: International Conference on Machine Learning (2006)

  19. Like, Z., Shunyi, Z., Hao, M., Xiaonan, W., Haitao, W.: Research on the number recognition based on athlete number plate image. J. East China Normal Univ. 3, 64–77 (2017). (In Chinese)

    Google Scholar 

Download references

Acknowledgements

This research was supported by Scientific and Technological Development Program Foundation of Jilin Province, China (Nos. 201604054YY; 20170414006GH).

Author information

Authors and Affiliations

  1. College of Computer Science and Technology, Key Laboratory of Symbolic Computation and Knowledge, Jilin University, Changchun, China

    Xin Wang

  2. College of Computer Science and Technology, Jilin University, Changchun, China

    Junxiang Yang

Authors
  1. Xin Wang
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Junxiang Yang
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Xin Wang.

Additional information

Publisher's Note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, X., Yang, J. Marathon athletes number recognition model with compound deep neural network. SIViP 14, 1379–1386 (2020). https://doi.org/10.1007/s11760-020-01677-5

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-020-01677-5

Keywords