Skip to main content

Deep Reader: Information Extraction from Document Images via Relation Extraction and Natural Language

  • Conference paper
  • First Online:
Computer Vision – ACCV 2018 Workshops (ACCV 2018)

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

Included in the following conference series:

Abstract

Recent advancements in the area of Computer Vision with state-of-art Neural Networks has given a boost to Optical Character Recognition (OCR) accuracies. However, extracting characters/text alone is often insufficient for relevant information extraction as documents also have a visual structure that is not captured by OCR. Extracting information from tables, charts, footnotes, boxes, headings and retrieving the corresponding structured representation for the document remains a challenge and finds application in a large number of real-world use cases. In this paper, we propose a novel enterprise based end-to-end framework called DeepReader which facilitates information extraction from document images via identification of visual entities and populating a meta relational model across different entities in the document image. The model schema allows for an easy to understand abstraction of the entities detected by the deep vision models and the relationships between them. DeepReader has a suite of state-of-the-art vision algorithms which are applied to recognize handwritten and printed text, eliminate noisy effects, identify the type of documents and detect visual entities like tables, lines and boxes. Deep Reader maps the extracted entities into a rich relational schema so as to capture all the relevant relationships between entities (words, textboxes, lines etc.) detected in the document. Relevant information and fields can then be extracted from the document by writing SQL queries on top of the relationship tables. A natural language based interface is added on top of the relationship schema so that a non-technical user, specifying the queries in natural language, can fetch the information with minimal effort. In this paper, we also demonstrate many different capabilities of Deep Reader and report results on a real-world use case.

D. Vishwanath and R. Rahul have contributed equally.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 69.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 89.99
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

Similar content being viewed by others

Notes

  1. 1.

    https://www.kaggle.com/c/denoising-dirty-documents.

References

  1. Peanho, C.A., Stagni, H., da Silva, F.S.C.: Semantic information extraction from images of complex documents. Appl. Intell. 37, 543–557 (2012)

    Article  Google Scholar 

  2. 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 

  3. Cesarini, F., Gori, M., Marinai, S., Soda, G.: Informys: a flexible invoice-like form-reader system. IEEE Trans. Pattern Anal. Mach. Intell. 20, 730–745 (1998)

    Article  Google Scholar 

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

    Google Scholar 

  5. Hammami, M., Héroux, P., Adam, S., d’Andecy, V.P.: One-shot field spotting on colored forms using subgraph isomorphism. In: International Conference on Document Analysis and Recognition (2015)

    Google Scholar 

  6. Kooli, N., Belaïd, A.: Semantic label and structure model based approach for entity recognition in database context. In: 13th International Conference on Document Analysis and Recognition (ICDAR), pp. 301–305. IEEE (2015)

    Google Scholar 

  7. Aldavert, D., Rusinol, M., Toledo, R.: Automatic static/variable content separation in administrative document images. In: 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), pp. 87–92. IEEE (2017)

    Google Scholar 

  8. Breuel, T.M.: A practical, globally optimal algorithm for geometric matching under uncertainty. Electron. Notes Theor. Comput. Sci. 46, 188–202 (2001)

    Article  Google Scholar 

  9. Breuel, T.M.: High performance document layout analysis. In: Proceedings of the Symposium on Document Image Understanding Technology, pp. 209–218 (2003)

    Google Scholar 

  10. 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, pp. 327–331. IEEE (2007)

    Google Scholar 

  11. Schulz, F., Ebbecke, M., Gillmann, M., Adrian, B., Agne, S., Dengel, A.: Seizing the treasure: transferring knowledge in invoice analysis. In: 10th International Conference on Document Analysis and Recognition, ICDAR 2009, pp. 848–852. IEEE (2009)

    Google Scholar 

  12. Goodfellow, I., et al.: arxiv: 1406.2661 (2014)

  13. Koch, G., Zemel, R., Salakhutdinov, R.: Siamese neural networks for one-shot image recognition. In: ICML Deep Learning Workshop, vol. 2 (2015)

    Google Scholar 

  14. Chowdhury, A., Vig, L.: An efficient end-to-end neural model for handwritten text recognition. arXiv preprint arXiv:1807.07965 (2018)

  15. Vinyals, O., Le, Q.: A neural conversational model. arXiv preprint arXiv:1506.05869 (2015)

  16. Yu, T., Li, Z., Zhang, Z., Zhang, R., Radev, D.: TypeSQL: knowledge-based type-aware neural text-to-SQL generation. arXiv preprint arXiv:1804.09769 (2018)

  17. Ledig, C., et al.: Photo-realistic single image super-resolution using a generative adversarial network. arXiv preprint (2017)

    Google Scholar 

  18. Li, C., Wand, M.: Precomputed real-time texture synthesis with Markovian generative adversarial networks. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9907, pp. 702–716. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46487-9_43

    Chapter  Google Scholar 

  19. Bousmalis, K., Silberman, N., Dohan, D., Erhan, D., Krishnan, D.: Unsupervised pixel-level domain adaptation with generative adversarial networks. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), vol. 1, p. 7 (2017)

    Google Scholar 

  20. Zhang, H., Sindagi, V., Patel, V.M.: Image de-raining using a conditional generative adversarial network. arXiv preprint arXiv:1701.05957 (2017)

  21. Frank, A.: UCI machine learning repository. University of California, School of Information and Computer Science, Irvine (2010). http://archive.ics.uci.edu/ml

  22. Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13, 600–612 (2004)

    Article  Google Scholar 

  23. Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 539–546 (2005)

    Google Scholar 

  24. Chowdhury, A., Lovekesh, V.: An efficient end-to-end neural model for handwritten text recognition (2018)

    Google Scholar 

  25. Tian, Z., Huang, W., He, T., He, P., Qiao, Y.: Detecting text in natural image with connectionist text proposal network. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9912, pp. 56–72. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46484-8_4

    Chapter  Google Scholar 

  26. Mikolov, T., Karafiát, M., Burget, L., Černockỳ, J., Khudanpur, S.: Recurrent neural network based language model. In: Eleventh Annual Conference of the International Speech Communication Association (2010)

    Google Scholar 

  27. Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9, 1735–1780 (1997)

    Article  Google Scholar 

  28. Lafferty, J., McCallum, A., Pereira, F.C.: Conditional random fields: probabilistic models for segmenting and labeling sequence data (2001)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Rohit Rahul , Gunjan Sehgal , Swati , Arindam Chowdhury , Monika Sharma , Lovekesh Vig , Gautam Shroff or Ashwin Srinivasan .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Vishwanath, D. et al. (2019). Deep Reader: Information Extraction from Document Images via Relation Extraction and Natural Language. 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_15

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-21074-8_15

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-21073-1

  • Online ISBN: 978-3-030-21074-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics