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Information Extraction from Invoices

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Document Analysis and Recognition – ICDAR 2021 (ICDAR 2021)

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

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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.

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Notes

  1. 1.

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

  2. 2.

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

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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.

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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
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  2. Elodie Carel
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  3. Aurélie Joseph
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  4. Mickael Coustaty
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  5. Antoine Doucet
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Corresponding author

Correspondence to Ahmed Hamdi .

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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

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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

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

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  • Online ISBN: 978-3-030-86331-9

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