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Kerstin Enflo. Photo.

Kerstin Enflo

Professor

Kerstin Enflo. Photo.

Reading the ransom: Methodological advancements in extracting the Swedish Wealth Tax of 1571

Author

  • Christopher Blomqvist
  • Kerstin Enflo
  • Andreas Jakobsson
  • Kalle Åström

Summary, in English

We describe a deep learning method to read hand-written records from the 16th century. The method consists of a combination of a segmentation module and a Handwritten Text Recognition (HTR) module. The transformer-based HTR module exploits both language and image features in reading, classifying and extracting the position of each word on the page. The method is demonstrated on a unique historical document: The Swedish Wealth Tax of 1571. Results suggest that the segmentation module performs significantly better than the lay-out analysis implemented in state-of-the art programs, enabling us to trace many more text blocks correctly on each page. The HTR module has a low character error rate (CER), in addition to being able to classify words and help organize them into tabular formats. By demonstrating an automated process to transform loosely structured handwritten information from the 16th century into organized tables, our method should interest economic historians seeking to digitize and organize quantitative material from pre-industrial periods.

Department/s

  • Department of Economic History
  • Growth, technological change, and inequality
  • LTH Profile Area: AI and Digitalization
  • LTH Profile Area: Engineering Health
  • eSSENCE: The e-Science Collaboration
  • Mathematical Statistics
  • Biomedical Modelling and Computation
  • Statistical Signal Processing Group
  • Stroke Imaging Research group
  • Mathematics (Faculty of Engineering)
  • ELLIIT: the Linköping-Lund initiative on IT and mobile communication
  • Mathematical Imaging Group

Publishing year

2023

Language

English

Publication/Series

Explorations in Economic History

Volume

87

Document type

Journal article

Publisher

Elsevier

Topic

  • Economic History

Status

Published

Project

  • Praise the people or praise the place: How culture and specialization drive long-term regional growth

Research group

  • Biomedical Modelling and Computation
  • Statistical Signal Processing Group
  • Stroke Imaging Research group
  • Mathematical Imaging Group

ISBN/ISSN/Other

  • ISSN: 0014-4983