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Asian Conference on Computer Vision

ACCV 2018: Computer Vision – ACCV 2018 Workshops pp 144–158Cite as

An Invoice Reading System Using a Graph Convolutional Network

Part of the Lecture Notes in Computer Science book series (LNIP,volume 11367)

Abstract

In this paper, we present a model-free system for reading digitized invoice images, which highlights the most useful billing entities and does not require any particular parameterization. The power of the system lies in the fact that it generalizes to both seen and unseen layouts of invoice. The system first breaks down the invoice data into various set of entities to extract and then learns structural and semantic information for each entity to extract via a graph structure, which is later generalized to the whole invoice structure. This local neighborhood exploitation is accomplished via a Graph Convolutional Network (GCN). The system digs deep to extract table information and provide complete invoice reading upto 27 entities of interest without any template information or configuration with an excellent overall F-measure score of 0.93.

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References

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

    CrossRef  Google Scholar 

  2. Schuster, D., et al.: Intellix-end-user trained information extraction for document archiving. In: 2013 12th International Conference on Document Analysis and Recognition (ICDAR), pp. 101–105. IEEE (2013)

    Google Scholar 

  3. Rusinol, M., Benkhelfallah, T., Poulain dAndecy, V.: Field extraction from administrative documents by incremental structural templates. In: 2013 12th International Conference on Document Analysis and Recognition (ICDAR), pp. 1100–1104. IEEE (2013)

    Google Scholar 

  4. Hamza, H., Belaïd, Y., Belaïd, A.: A case-based reasoning approach for invoice structure extraction. In: Ninth International Conference on Document Analysis and Recognition, ICDAR 2007, vol. 1, 327–331. IEEE (2007)

    Google Scholar 

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

    CrossRef  MATH  Google Scholar 

  6. Cesarini, F., Francesconi, E., Gori, M., Soda, G.: Analysis and understanding of multi-class invoices. Doc. Anal. Recogn. 6, 102–114 (2003)

    CrossRef  Google Scholar 

  7. d’Andecy, V.P., Hartmann, E., Rusiñol, M.: Field extraction by hybrid incremental and a-priori structural templates. In: 2018 13th IAPR International Workshop on Document Analysis Systems (DAS), pp. 251–256. IEEE (2018)

    Google Scholar 

  8. Palm, R.B., Winther, O., Laws, F.: Cloudscan-a configuration-free invoice analysis system using recurrent neural networks. In: 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), pp. 406–413. IEEE (2017)

    Google Scholar 

  9. Kasar, T., Bhowmik, T.K., Belaid, A.: Table information extraction and structure recognition using query patterns. In: 2015 13th International Conference on Document Analysis and Recognition (ICDAR), pp. 1086–1090. IEEE (2015)

    Google Scholar 

  10. Santosh, K., Belaïd, A.: Document information extraction and its evaluation based on client’s relevance. In: 2013 12th International Conference on Document Analysis and Recognition (ICDAR), pp. 35–39. IEEE (2013)

    Google Scholar 

  11. Sennrich, R., Haddow, B., Birch, A.: Neural machine translation of rare words with subword units. In: 54th Annual Meeting of the Association for Computational Linguistics, pp. 1715–1725 (2016)

    Google Scholar 

  12. Heinzerling, B., Strube, M.: BPEmb: tokenization-free pre-trained subword embeddings in 275 languages. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki (2018)

    Google Scholar 

  13. Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: ICLR (2017)

    Google Scholar 

  14. Defferrard, M., Bresson, X., Vandergheynst, P.: Convolutional neural networks on graphs with fast localized spectral filtering. In: Advances in Neural Information Processing Systems, pp. 3844–3852 (2016)

    Google Scholar 

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

    Google Scholar 

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

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Author information

Authors and Affiliations

  1. MOSIG program, GVR, INP, 38000, Grenoble, France

    D. Lohani

  2. Université de Lorraine-CNRS-LORIA, Campus scientifique, 54500, Vandoeuvre-lès-Nancy, France

    A. Belaïd & Y. Belaïd

Authors
  1. D. Lohani
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  2. A. Belaïd
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  3. Y. Belaïd
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Corresponding author

Correspondence to A. Belaïd .

Editor information

Editors and Affiliations

  1. School of Computer Science, University of Adelaide, Adelaide, Australia

    Prof. Gustavo Carneiro

  2. Data61, Commonwealth Scientific and Industrial Research Organization, Canberra, Australia

    Dr. Shaodi You

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Lohani, D., Belaïd, A., Belaïd, Y. (2019). An Invoice Reading System Using a Graph Convolutional Network. In: Carneiro, G., You, S. (eds) Computer Vision – ACCV 2018 Workshops. ACCV 2018. Lecture Notes in Computer Science(), vol 11367. Springer, Cham. https://doi.org/10.1007/978-3-030-21074-8_12

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  • DOI: https://doi.org/10.1007/978-3-030-21074-8_12

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