DocLightDetect: A New Algorithm for Occlusion Classification in Identification Documents | SpringerLink
Skip to main content
Springer Nature Link
Log in

DocLightDetect: A New Algorithm for Occlusion Classification in Identification Documents

  • Conference paper
  • First Online:
Document Analysis Systems (DAS 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14994))

Included in the following conference series:

  • 212 Accesses

Abstract

In the current digital era, organizations primarily interact with their clients and users online. However, accurately identifying these digital users in the physical realm raises significant challenges. Several entities, including financial institutions, insurance companies, and government services, require photos of documents sent through mobile applications to associate the physical and digital personas. This procedure entails significant computational challenges, mainly due to the need for adequate user guidance when capturing images and the variability of devices. User dependence often results in occlusions in images caused by various factors such as human fingers, shadows, and the spotlight effect. The latter is particularly common and complex due to using the device’s flash. While previous research has focused on automatically identifying occlusions caused by human fingers, the present work focuses on occlusions caused by the spotlight effect. We propose a new algorithm, DocLightDetect, which uses image segmentation as a preprocessing step to improve the accuracy of classifying occlusions caused by the spotlight effect in identification documents. The effectiveness of DocLightDetect is demonstrated through the new SpotBID Set dataset. The proposed algorithm improves performance compared to state-of-the-art document occlusion classification techniques. It is also optimized for low computational cost, making it suitable for applications in mobile devices, robotics, and the Internet of Things (IoT).

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
JPY 3498
Price includes VAT (Japan)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
JPY 8465
Price includes VAT (Japan)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
JPY 10581
Price includes VAT (Japan)
  • 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

Similar content being viewed by others

Notes

  1. 1.

    SpotBid Set.

References

  1. Gai, K., Qiu, M., Sun, X.: A survey on FinTech. J. Netw. Comput. Appl. 103, 262–273 (2018)

    Article  Google Scholar 

  2. Rodriguez-Segura, D.: EdTech in developing countries: a review of the evidence. The World Bank Res. Observer 37, 171–203 (2022)

    Article  Google Scholar 

  3. Nurazizah, A., Novita, N.: Healthtech startups internal control to increase competitive advantage in the new normal era. Jurnal Akuntansi 11, 105–122 (2021)

    Article  Google Scholar 

  4. Ostrowska, M.: Regulation of InsurTech: is the principle of proportionality an answer? Risks 9, 185 (2021)

    Article  Google Scholar 

  5. Bharosa, N.: The rise of GovTech: trojan horse or blessing in disguise? A research agenda. Gov. Inf. Q. 39(3), 101692 (2022)

    Article  Google Scholar 

  6. Neves, R., Verçosa, L., Macêdo, D., Bezerra, B., Zanchettin, C.: A fast fully octave convolutional neural network for document image segmentation. In: 2020 International Joint Conference On Neural Networks (IJCNN), pp. 1–6 (2020)

    Google Scholar 

  7. Neves, R., Lima, E., Bezerra, B., Zanchettin, C., Toselli, A.: HU-PageScan: a fully convolutional neural network for document page crop. IET Image Process. 14, 3890–3898 (2020)

    Article  Google Scholar 

  8. Kingma, D., Ba, J.: Adam: a method for stochastic optimization. arXiv Preprint arXiv:1412.6980 (2014)

  9. Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28

    Chapter  Google Scholar 

  10. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv Preprint arXiv:1409.1556 (2014)

  11. das Neves Junior, R.B., Nascimento, S., Bezerra, B.L.D.: A robust approach to detect occlusions during camera-based document scanning. In: 9th IEEE Latin American Conference on Computational Intelligence (2023)

    Google Scholar 

  12. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)

    Google Scholar 

  13. Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700–4708 (2017)

    Google Scholar 

  14. Tan, M., Le, Q.: EfficientNet: rethinking model scaling for convolutional neural networks. In: International Conference on Machine Learning, pp. 6105–6114 (2019)

    Google Scholar 

  15. Mullins, R., Ahearne, M., Lam, S., Hall, Z., Boichuk, J.: Know your customer: how salesperson perceptions of customer relationship quality form and influence account profitability. J. Mark. 78, 38–58 (2014)

    Article  Google Scholar 

  16. Ota, K., Dao, M., Mezaris, V., Natale, F.: Deep learning for mobile multimedia: a survey. ACM Trans. Multimedia Comput. Commun. Appl. (TOMM) 13, 1–22 (2017)

    Google Scholar 

  17. Geovanna Soares, A., Leite Dantas Bezerra, B., Baptista Lima, E.: How far deep learning systems for text detection and recognition in natural scenes are affected by occlusion? In: Barney Smith, E.H., Pal, U. (eds.) ICDAR 2021. LNCS, vol. 12916, pp. 198–212. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-86198-8_15

    Chapter  Google Scholar 

  18. Zhou, X., et al.: EAST: an efficient and accurate scene text detector. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5551–5560 (2017)

    Google Scholar 

  19. Wang, W., et al.: Shape robust text detection with progressive scale expansion network. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9336–9345 (2019)

    Google Scholar 

  20. Baek, Y., Lee, B., Han, D., Yun, S., Lee, H.: Character region awareness for text detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9365–9374 (2019)

    Google Scholar 

  21. Liu, W., Chen, C., Wong, K., Su, Z., Han, J.: STAR-Net: a spatial attention residue network for scene text recognition. In: BMVC, vol. 2, p. 7 (2016)

    Google Scholar 

  22. 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, 2298–2304 (2016)

    Article  Google Scholar 

  23. Wang, W., et al.: Efficient and accurate arbitrary-shaped text detection with pixel aggregation network. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 8440–8449 (2019)

    Google Scholar 

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

    Google Scholar 

  25. Borisyuk, F., Gordo, A., Sivakumar, V.: Rosetta: large scale system for text detection and recognition in images. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 71–79 (2018)

    Google Scholar 

  26. Sá Soares, A., Neves Junior, R., Bezerra, B.: BID dataset: a challenge dataset for document processing tasks. In: Anais Estendidos do XXXIII Conference on Graphics, Patterns and Images, pp. 143–146 (2020)

    Google Scholar 

  27. Lopes Junior, C.A.M., das Neves Junior, R.B., Bezerra, B.L.D., Toselli, A.H., Impedovo, D.: ICDAR 2021 competition on components segmentation task of document photos. In: Lladós, J., Lopresti, D., Uchida, S. (eds.) ICDAR 2021. LNCS, vol. 12824, pp. 678–692. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-86337-1_45

    Chapter  Google Scholar 

  28. Lin, M., Chen, Q., Yan, S.: Network in network. arXiv Preprint arXiv:1312.4400 (2013)

  29. Nair, V., Hinton, G.: Rectified linear units improve restricted boltzmann machines. In: Proceedings of the 27th International Conference on Machine Learning (ICML-2010), pp. 807–814 (2010)

    Google Scholar 

  30. LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86, 2278–2324 (1998)

    Article  Google Scholar 

  31. Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press, Cambridge (2016)

    Google Scholar 

  32. Burie, J., et al.: ICDAR2015 competition on smartphone document capture and OCR (SmartDoc). In: 2015 13th International Conference on Document Analysis and Recognition (ICDAR), pp. 1161–1165 (2015)

    Google Scholar 

  33. He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017)

    Google Scholar 

  34. Malkauthekar, M.: Analysis of Euclidean distance and Manhattan distance measure in face recognition. In: Third International Conference on Computational Intelligence and Information Technology (CIIT 2013), pp. 503–507 (2013)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Escola Politécnica de Pernambuco, Universidade de Pernambuco, Recife, Brazil

    Ricardo Batista das Neves Junior & Byron Leite Dantas Bezerra

  2. Centro de Informática, Universidade Federal de Pernambuco, Recife, Brazil

    Cleber Zanchettin

Authors
  1. Ricardo Batista das Neves Junior
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Byron Leite Dantas Bezerra
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Cleber Zanchettin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ricardo Batista das Neves Junior .

Editor information

Editors and Affiliations

  1. University of West Attica, Egaleo, Greece

    Giorgos Sfikas

  2. National Technical University of Athens, Zografou, Greece

    George Retsinas

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

das Neves Junior, R.B., Dantas Bezerra, B.L., Zanchettin, C. (2024). DocLightDetect: A New Algorithm for Occlusion Classification in Identification Documents. In: Sfikas, G., Retsinas, G. (eds) Document Analysis Systems. DAS 2024. Lecture Notes in Computer Science, vol 14994. Springer, Cham. https://doi.org/10.1007/978-3-031-70442-0_12

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-70442-0_12

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-70441-3

  • Online ISBN: 978-3-031-70442-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Societies and partnerships

Search

Navigation