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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)


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.


  • 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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    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.

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    Deceased people database since 1970 (INSEE, in French):

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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.

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