Applications of machine learning in tabular document digitisation

Christian M. Dahl*, Torben S. D. Johansen, Emil N Sorensen, Christian E. Westermann, Simon Wittrock

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

1 Citation (Scopus)

Abstract

Data acquisition forms the primary step in all empirical research. The availability of data directly impacts the quality and extent of conclusions and insights. In particular, larger and more detailed datasets provide convincing answers even to complex research questions. The main problem is that large and detailed usually imply costly and difficult, especially when the data medium is paper and books. Human operators and manual transcription has been the traditional approach for collecting historical data. We instead advocate the use of modern machine learning techniques to automate the digitization and transcription process. We propose a customizable end-to-end transcription pipeline to perform layout classification, table segmentation, and transcribe handwritten text that is suitable for tabular data, as is common in, e.g., census lists and birth and death records. We showcase our pipeline through two applications: The first demonstrates that unsupervised layout classification applied to raw scans of nurse journals can be used to obtain valuable insights into an extended nurse home visiting program. The second application uses attention-based neural networks for handwritten text recognition to transcribe age and birth and death dates and includes a comparison to automated transcription using Transkribus in the regime of tabular data. We describe each step in our pipeline and provide implementation insights.
Original languageEnglish
Pages (from-to)34-48
Number of pages15
JournalHistorical Methods
Volume56
Issue number1
DOIs
Publication statusPublished - 19 Jan 2023

Bibliographical note

Funding Information:
E.N. Sørensen gratefully acknowledges financial support from the European Research Council (Starting Grant Reference 851725). We also gratefully acknowledge support from DFF who has funded the research project “Inside the black box of welfare state expansion: Early-life health policies, parental investments and socio-economic and health trajectories” (grant 8106-00003B) with PI Miriam Wüst. We thank Peter Sandholdt Jensen, Joseph Price, Michael Rosholm, an editor, and three anonymous referees for valuable comments and suggestions that have improved the manuscript substantially. We also thank Søren Poder for his expertise on digitisation of historical documents. We gratefully acknowledge support from Rigsarkivet (Danish National Archive) and Aarhus Stadsarkiv (Aarhus City Archive) who have supplied large amounts of scanned source material. Data (excluding nurse records) and code are available on request.

Publisher Copyright:
© 2023 Taylor & Francis Group, LLC.

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