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International Workshop on Document Analysis Systems

DAS 2022: Document Analysis Systems pp 143–157Cite as

Recognition and Information Extraction in Historical Handwritten Tables: Toward Understanding Early \(20^{th}\) Century Paris Census

Part of the Lecture Notes in Computer Science book series (LNCS,volume 13237)

Abstract

We aim to build a vast database (up to 9 million individuals) from the handwritten tabular nominal census of Paris of 1926, 1931 and 1936, each composed of about 100,000 handwritten simple pages in a tabular format. We created a complete pipeline that goes from the scan of double pages to text prediction while minimizing the need for segmentation labels. We describe how weighted finite state transducers, writer specialization and self-training further improved our results. We also introduce through this communication two annotated datasets for handwriting recognition that are now publicly available, and an open-source toolkit to apply WFST on CTC lattices.

Keywords

  • Handwriting recognition
  • Document layout analysis
  • Self-training
  • Table analysis
  • WFST
  • Semi-supervised learning

Project supported by CollEx-Persée (AAP19_20), with the financial collaboration of the TGIR Progedo and the Grand Équipement Documentaire Campus Condorcet.

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Notes

  1. 1.

    https://gitlab.com/projet-popp/sigra/.

  2. 2.

    https://github.com/Shulk97/POPP-datasets/.

  3. 3.

    The complete Belleville census was written by three writers but their writing style are very similar and can be therefore considered as one unique writing style.

  4. 4.

    https://github.com/Shulk97/POPP-datasets/.

  5. 5.

    https://github.com/dhlab-epfl/dhSegment.

  6. 6.

    https://github.com/FactoDeepLearning/VerticalAttentionOCR.

  7. 7.

    Deceased people database since 1970 (INSEE, in French): https://www.insee.fr/fr/information/4190491.

  8. 8.

    http://www.toponymiefrancophone.org/divfranco/Bougainville/Liste_generale.aspx?nom=liste_pays.

  9. 9.

    https://gitlab.com/projet-popp/sigra/.

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Author information

Authors and Affiliations

  1. LITIS EA4108, University of Rouen Normandy, Rouen, France

    Thomas Constum, Nicolas Kempf, Thierry Paquet & Pierrick Tranouez

  2. LITIS EA4108, INSA Rouen Normandy, Saint-Étienne-du-Rouvray, France

    Clément Chatelain

  3. LARHRA, UMR 5190, CNRS, Lyon, France

    Sandra Brée

  4. Campus Condorcet - The Grand Équipement Documentaire, Aubervilliers, France

    François Merveille

Authors
  1. Thomas Constum
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  2. Nicolas Kempf
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  5. Clément Chatelain
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Correspondence to Thomas Constum .

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Editors and Affiliations

  1. Kyushu University, Fukuoka, Japan

    Seiichi Uchida

  2. Boise State University, BOISE, ID, USA

    Elisa Barney

  3. LIRIS UMR CNRS, Villeurbanne, France

    Véronique Eglin

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Constum, T. et al. (2022). Recognition and Information Extraction in Historical Handwritten Tables: Toward Understanding Early \(20^{th}\) Century Paris Census. In: Uchida, S., Barney, E., Eglin, V. (eds) Document Analysis Systems. DAS 2022. Lecture Notes in Computer Science, vol 13237. Springer, Cham. https://doi.org/10.1007/978-3-031-06555-2_10

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  • DOI: https://doi.org/10.1007/978-3-031-06555-2_10

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