Skip to main content

Running to Get Recognised

  • 743 Accesses

Part of the Communications in Computer and Information Science book series (CCIS,volume 1365)


This research investigates the use of Convolutional Neural Networks (CNN) and specifically, You Only Look Once ver. 4 (YOLOv4) to detect Racing Bib Numbers (RBNs) in images from running races and then to recognise the actual numbers using Optical Character Recognition (OCR) techniques. Pre-processing and Tesseract OCR were employed to achieve this. Using a self-acquired private dataset we achieve a recall of 0.91, precision of 0.88, an F1-measure of 0.89, and mean average precision (mAP) of 0.935 for detection. Full number recognition of 71% is then achieved on the successfully detected RBNs. Additionally, the proposed approach attains a very low average inference time of 23.5 ms compared to a previous best recorded time of 750 ms. This is achieved this with a relatively small training set of 1374 images, where previous research used 498,385 labelled images.


  • Machine vision
  • Convolutional Neural Networks
  • Racing Bib Number

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

Buying options

USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
USD   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
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

Learn about institutional subscriptions


  1. 1.

    The YOLOv4 repository is available at:

  2. 2.

    The Tesseract OCR repository is stored at:

  3. 3.

    RBNR dataset is available at:


  1. Alexey, A.B.: AlexeyAB/darknet: Windows and Linux version of Darknet Yolo v3 v2 Neural Networks for object detection (Tensor Cores are used) (2019). Accessed 4 Oct 2020

  2. Ben-Ami, I., Basha, T., Avidan, S.: Racing bib number recognition. In: BMVC 2012 - Electronic Proceedings of the British Machine Vision Conference 2012. Tel Aviv University (2012)

    Google Scholar 

  3. Bochkovskiy, A., Wang, C.Y., Liao, H.Y.M.: YOLOv4: optimal speed and accuracy of object detection (2020).

  4. Boonsim, N.: Racing bib number localization on complex backgrounds. Technical reports (2018)

    Google Scholar 

  5. Boonsim, N., Kanjaruek, S.: Racing bib number localization based on region convolutional neural networks. In: Proceedings of 2018 the 8th International Workshop on Computer Science and Engineering, pp. 293–297 (2018)

    Google Scholar 

  6. Everingham, M., Gool, L.V., Williams, C.K.I., Winn, J., Zisserman, A.: The pascal visual object classes (VOC) challenge. Int. J. Comput. Vis. 88(2), 303–338 (2010)

    CrossRef  Google Scholar 

  7. Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 580–587. IEEE Computer Society (2014)

    Google Scholar 

  8. Goodfellow, I.J., Bulatov, Y., Ibarz, J., Arnoud, S., Shet, V.: Multi-digit number recognition from street view imagery using deep convolutional neural networks (2014).

  9. IAAF: Habos Gebrihwet lap miscount - IAAF Diamond League (2019). Accessed 4 Oct 2020

  10. Ivarsson, E., Mueller, R.M.: Racing bib number recognition using deep learning racing bib number recognition using deep learning completed research. Technical reports, Berlin School of Economics and Law (2019)

    Google Scholar 

  11. Lienhart, R., Maydt, J.: An extended set of Haar-like features for rapid object detection. In: IEEE International Conference on Image Processing, vol. 1 (2002)

    Google Scholar 

  12. Tsung-Yi, L., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740–755. Springer, Cham (2014).

    CrossRef  Google Scholar 

  13. Netzer, Y., Wang, T., Coates, A., Bissacco, A., Wu, B., Ng, A.Y.: Reading digits in natural images with unsupervised feature learning. Technical reports (2011),

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

  15. Roy, S., Shivakumara, P., Mondal, P., Raghavendra, R., Pal, U., Lu, T.: A new multi-modal technique for bib number/text detection in natural images. In: Ho, Y.-S., Sang, J., Ro, Y.M., Kim, J., Wu, F. (eds.) PCM 2015. LNCS, vol. 9314, pp. 483–494. Springer, Cham (2015).

    CrossRef  Google Scholar 

  16. Shivakumara, P., Quy Phan, T., Lim Tan, C.: New wavelet and color features for text detection in video (2010)

    Google Scholar 

  17. Shivakumara, P., Raghavendra, R., Qin, L., Raja, K.B., Lu, T., Pal, U.: A new multi-modal approach to bib number/text detection and recognition in marathon images. Pattern Recognit. 61, 479–491 (2017)

    CrossRef  Google Scholar 

  18. Smith, R.: An overview of the tesseract OCR engine. In: Proceedings of the International Conference on Document Analysis and Recognition, ICDAR, vol. 2, pp. 629–633 (2007)

    Google Scholar 

  19. Wong, Y.: Deep learning based racing bib number detection and recognition. Jordanian J. Comput. Inf. Technol. 05(03), 1 (2019)

    Google Scholar 

  20. Wrońska, A., Sarnacki, K., Saeed, K.: Athlete number detection on the basis of their face images. In: 2017 International Conference on Biometrics and Kansei Engineering (ICBAKE), pp. 84–89. IEEE (2017)

    Google Scholar 

  21. Yang, C.S., Yang, Y.H.: Improved local binary pattern for real scene optical character recognition. Pattern Recognit. Lett. 100, 14–21 (2017)

    CrossRef  Google Scholar 

  22. Yun, S., Han, D., Oh, S.J., Chun, S., Choe, J., Yoo, Y.: Cutmix: Regularization strategy to train strong classifiers with localizable features. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 6023–6032 (2019)

    Google Scholar 

Download references


Avaya funded the course of study that gave rise to this research. The authors also wish to acknowledge the DJEI/DES/SFI/HEA Irish Centre for High-End Computing (ICHEC) for the provision of computational facilities and support. Access to the training images was provided by the Galway County Athletics board and the photographer John O’Connor.

Author information

Authors and Affiliations


Corresponding authors

Correspondence to Gerard Carty , Muhammad Adil Raja or Conor Ryan .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2021 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Carty, G., Raja, M.A., Ryan, C. (2021). Running to Get Recognised. In: Thampi, S.M., Krishnan, S., Hegde, R.M., Ciuonzo, D., Hanne, T., Kannan R., J. (eds) Advances in Signal Processing and Intelligent Recognition Systems. SIRS 2020. Communications in Computer and Information Science, vol 1365. Springer, Singapore.

Download citation

  • DOI:

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-0424-9

  • Online ISBN: 978-981-16-0425-6

  • eBook Packages: Computer ScienceComputer Science (R0)