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Digitizing Historical Balance Sheet Data: A Practitioner's Guide

Author

Listed:
  • 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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    Cited by:

    1. is not listed on IDEAS
    2. Markus Brunnermeier & Sergio Correia & Stephan Luck & Emil Verner & Tom Zimmermann, 2025. "The Debt-Inflation Channel of the German (Hyper) Inflation," American Economic Review, American Economic Association, vol. 115(7), pages 2111-2150, July.
    3. Sergio A. Correia & Stephan Luck & Emil Verner, 2024. "Failing Banks," Staff Reports 1117, Federal Reserve Bank of New York.
    4. Davide Cipullo & Luca V.A. Colombo & Michele Magnani & Massimiliano Gaetano Onorato, 2025. "Historical Newspaper Markets," CESifo Working Paper Series 12194, CESifo.
    5. Huixin Bi & Nicolas Petrosky-Nadeau & Nora Traum & Greg Woodward, 2025. "Worker and Firm Search in the Labor Market: Evidence from Classified Advertisements," Research Working Paper RWP 25-07, Federal Reserve Bank of Kansas City.

    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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