Skip to main content
SpringerLink
Log in

Information Extraction from Invoices

  • Conference paper
  • First Online:
Document Analysis and Recognition – ICDAR 2021 (ICDAR 2021)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12822))

Included in the following conference series:

Abstract

The present paper is focused on information extraction from key fields of invoices using two different methods based on sequence labeling. Invoices are semi-structured documents in which data can be located based on the context. Common information extraction systems are model-driven, using heuristics and lists of trigger words curated by domain experts. Their performances are generally high on documents they have been trained for but processing new templates often requires new manual annotations, which is tedious and time-consuming to produce. Recent works on deep learning applied to business documents claimed a gain in terms of time and performance. While these systems do not need manual curation, they nevertheless require a large amount of data to achieve good results. In this paper, we present a series of experiments using neural networks approaches to study the trade-off between data requirements and performance in the extraction of information from key fields of invoices (such as dates, document numbers, types, amounts...). The main contribution of this paper is a system that achieves competitive results using a small amount of data compared to the state-of-the-art systems that need to be trained on large datasets, that are costly and impractical to produce in real-world applications.

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 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
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.

    All the examples/images used in this paper are fake for confidentiality reasons.

  2. 2.

    https://uber.github.io/ludwig/.

References

  1. Boroş, E., et al.: Alleviating digitization errors in named entity recognition for historical documents. In: Proceedings of the 24th Conference on Computational Natural Language Learning, pp. 431–441 (2020)

    Google Scholar 

  2. Chiu, J.P., Nichols, E.: Named entity recognition with bidirectional LSTM-CNNs. arXiv preprint arXiv:1511.08308 (2015)

  3. Collobert, R., Weston, J., Bottou, L., Karlen, M., Kavukcuoglu, K., Kuksa, P.: Natural language processing (almost) from scratch. J. Mach. Learn. Res. 12, 2493–2537 (2011)

    MATH  Google Scholar 

  4. Conneau, A., Lample, G.: Cross-lingual language model pretraining. In: Wallach, H., Larochelle, H., Beygelzimer, A., d’ Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 32, pp. 7059–7069. Curran Associates, Inc. (2019). http://papers.nips.cc/paper/8928-cross-lingual-language-model-pretraining.pdf

  5. Dengel, A.R., Klein, B.: smartFIX: a requirements-driven system for document analysis and understanding. In: Lopresti, D., Hu, J., Kashi, R. (eds.) International Workshop on Document Analysis Systems, DAS 2002. LNCS, vol. 2423, pp. 433–444. Springer, Heidelberg (2002). https://doi.org/10.1007/3-540-45869-7_47

  6. Dernoncourt, F., Lee, J.Y., Szolovits, P.: Neuroner: an easy-to-use program for named-entity recognition based on neural networks. arXiv preprint arXiv:1705.05487 (2017)

  7. Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)

  8. Grishman, R., Sundheim, B.M.: Message understanding conference-6: a brief history. In: COLING 1996 Volume 1: The 16th International Conference on Computational Linguistics (1996)

    Google Scholar 

  9. Holt, X., Chisholm, A.: Extracting structured data from invoices. In: Proceedings of the Australasian Language Technology Association Workshop 2018, pp. 53–59. Dunedin, New Zealand, December 2018

    Google Scholar 

  10. Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)

  11. Lample, G., Ballesteros, M., Subramanian, S., Kawakami, K., Dyer, C.: Neural architectures for named entity recognition. arXiv preprint arXiv:1603.01360 (2016)

  12. Lohani, D., Belaïd, A., Belaïd, Y.: An invoice reading system using a graph convolutional network. In: Carneiro, G., You, S. (eds.) ACCV 2018. LNCS, vol. 11367, pp. 144–158. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-21074-8_12

    Chapter  Google Scholar 

  13. Ma, X., Hovy, E.: End-to-end sequence labeling via bi-directional LSTM-CNNs-CRF. arXiv preprint arXiv:1603.01354 (2016)

  14. Martin, L., et al.: Camembert: a tasty French language model. arXiv preprint arXiv:1911.03894 (2019)

  15. Molino, P., Dudin, Y., Miryala, S.S.: Ludwig: a type-based declarative deep learning toolbox (2019)

    Google Scholar 

  16. Nadeau, D., Sekine, S.: A survey of named entity recognition and classification. Lingvisticae Investigationes 30(1), 3–26 (2007)

    Article  Google Scholar 

  17. Palm, R.B., Winther, O., Laws, F.: CloudScan - a configuration-free invoice analysis system using recurrent neural networks. CoRR abs/1708.07403 (2017), http://arxiv.org/abs/1708.07403

  18. Poulain d’Andecy, V., Hartmann, E., Rusinol, M.: Field extraction by hybrid incremental and a-priori structural templates. In: 13th IAPR International Workshop on Document Analysis Systems, DAS 2018, Vienna, Austria, 24–27 April 2018, pp. 251–256, April 2018. https://doi.org/10.1109/DAS.2018.29

  19. Radford, A., Narasimhan, K., Salimans, T., Sutskever, I.: Improving language understanding by generative pre-training (2018)

    Google Scholar 

  20. Reimers, N., Eckle-Kohler, J., Schnober, C., Kim, J., Gurevych, I.: GermEVAL-2014: nested named entity recognition with neural networks (2014)

    Google Scholar 

  21. Rusiñol, M., Benkhelfallah, T., D’Andecy, V.P.: Field extraction from administrative documents by incremental structural templates. In: ICDAR, pp. 1100–1104. IEEE Computer Society (2013). http://dblp.uni-trier.de/db/conf/icdar/icdar2013.html#RusinolBD13

  22. Sage, C., Aussem, A., Elghazel, H., Eglin, V., Espinas, J.: Recurrent neural network approach for table field extraction in business documents. In: 2019 International Conference on Document Analysis and Recognition, ICDAR 2019, Sydney, Australia, 20–25 September 2019, pp. 1308–1313, September 2019. https://doi.org/10.1109/ICDAR.2019.00211

  23. Sang, E.F., De Meulder, F.: Introduction to the CoNLL-2003 shared task: language-independent named entity recognition. arXiv preprint cs/0306050 (2003)

    Google Scholar 

  24. Vaswani, A., et al.: Attention is all you need. arXiv preprint arXiv:1706.03762 (2017)

  25. Wu, Y., et al.: Google’s neural machine translation system: bridging the gap between human and machine translation. arXiv preprint arXiv:1609.08144 (2016)

  26. Zhao, X., Niu, E., Wu, Z., Wang, X.: Cutie: learning to understand documents with convolutional universal text information extractor. arXiv preprint arXiv:1903.12363 (2019)

Download references

Acknowledgements

This work is supported by the Region Nouvelle Aquitaine under the grant number 2019-1R50120 (CRASD project) and AAPR2020-2019-8496610 (CRASD2 project), the European Union’s Horizon 2020 research and innovation program under grant 770299 (NewsEye) and by the LabCom IDEAS under the grant number ANR-18-LCV3-0008.

Author information

Authors and Affiliations

  1. Université de La Rochelle, L3i, Avenue Michel Crépeau, 17042, La Rochelle, France

    Ahmed Hamdi, Mickael Coustaty & Antoine Doucet

  2. Yooz, 1 Rue Fleming, 17000, La Rochelle, France

    Elodie Carel & Aurélie Joseph

Authors
  1. Ahmed Hamdi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Elodie Carel
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Aurélie Joseph
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mickael Coustaty
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Antoine Doucet
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ahmed Hamdi .

Editor information

Editors and Affiliations

  1. Universitat Autònoma de Barcelona, Barcelona, Spain

    Josep Lladós

  2. Lehigh University, Bethlehem, PA, USA

    Daniel Lopresti

  3. Kyushu University, Fukuoka-shi, Japan

    Seiichi Uchida

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Hamdi, A., Carel, E., Joseph, A., Coustaty, M., Doucet, A. (2021). Information Extraction from Invoices. In: Lladós, J., Lopresti, D., Uchida, S. (eds) Document Analysis and Recognition – ICDAR 2021. ICDAR 2021. Lecture Notes in Computer Science(), vol 12822. Springer, Cham. https://doi.org/10.1007/978-3-030-86331-9_45

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-86331-9_45

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-86330-2

  • Online ISBN: 978-3-030-86331-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Societies and partnerships

Search

Navigation