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Automated historical census digitization using image augmentation and transformer-based methods

Author

Listed:
  • Leonardo Costa Ribeiro

    (Federal University of Minas Gerais)

  • Jonatan Andersson

    (Uppsala University)

  • William Skoglund

    (Lund University)

  • Jakob Molinder

    (Uppsala University)

  • Martin Önnerfors

    (Uppsala University)

Abstract

A large literature in economic history uses digitized census data to study individual-level outcomes in history. Although many census records have been digitized manually, the process is extremely labor-intensive, and substantial material remains unprocessed in archives. Recent advances in machine learning offer the potential to automate large part of this work. We demonstrate an end-to-end digitization pipeline based on the transformer-based Donut model, trained on hand-annotated data and enhanced with image augmentation, to extract information from the 1955 Stockholm tax and census records. The resulting output attains high accuracy across multiple evaluation metrics.

Suggested Citation

  • Leonardo Costa Ribeiro & Jonatan Andersson & William Skoglund & Jakob Molinder & Martin Önnerfors, 2026. "Automated historical census digitization using image augmentation and transformer-based methods," Working Papers 0298, European Historical Economics Society (EHES).
    • Handle: RePEc:hes:wpaper:0298
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    File URL: https://ehes.org/wp/EHES_298.pdf
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    Keywords

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    JEL classification:

    • N01 - Economic History - - General - - - Development of the Discipline: Historiographical; Sources and Methods

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