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Digitizing Historical Balance Sheet Data: A Practitioner's Guide

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  • Sergio Correia
  • Stephan Luck

Abstract

This paper discusses how to successfully digitize large-scale historical micro-data by augmenting optical character recognition (OCR) engines with pre- and post-processing methods. Although OCR software has improved dramatically in recent years due to improvements in machine learning, off-the-shelf OCR applications still present high error rates which limit their applications for accurate extraction of structured information. Complementing OCR with additional methods can however dramatically increase its success rate, making it a powerful and cost-efficient tool for economic historians. This paper showcases these methods and explains why they are useful. We apply them against two large balance sheet datasets and introduce quipucamayoc, a Python package containing these methods in a unified framework.

Suggested Citation

  • Sergio Correia & Stephan Luck, 2022. "Digitizing Historical Balance Sheet Data: A Practitioner's Guide," Papers 2204.00052, arXiv.org, revised Jul 2022.
    • Handle: RePEc:arx:papers:2204.00052
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    References listed on IDEAS

    as
    1. Thomas Hegghammer, 2022. "OCR with Tesseract, Amazon Textract, and Google Document AI: a benchmarking experiment," Journal of Computational Social Science, Springer, vol. 5(1), pages 861-882, May.
    2. Mark Carlson & Sergio Correia & Stephan Luck, 2022. "The Effects of Banking Competition on Growth and Financial Stability: Evidence from the National Banking Era," Journal of Political Economy, University of Chicago Press, vol. 130(2), pages 462-520.
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    Cited by:

    1. Sergio A. Correia & Stephan Luck & Emil Verner, 2024. "Failing Banks," NBER Working Papers 32907, National Bureau of Economic Research, Inc.
    2. Markus K. Brunnermeier & Sergio A. Correia & Stephan Luck & Emil Verner & Tom Zimmermann, 2023. "The Debt-Inflation Channel of the German Hyperinflation," NBER Working Papers 31298, National Bureau of Economic Research, Inc.

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    More about this item

    JEL classification:

    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • C88 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Other Computer Software
    • N80 - Economic History - - Micro-Business History - - - General, International, or Comparative

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