# Digitizing Historical Balance Sheet Data: A Practitioner's Guide

arXiv: https://arxiv.org/abs/2204.00052 · HTML: https://mlsanity.com/paper/2204.00052/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 58 references that are in the map.
- First posted on arXiv 31 Mar 2022.

## Builds on (most cited first, 12 of 12)

- [LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding](https://mlsanity.com/paper/2012.14740/) (2020)
- [Image-based table recognition: data, model, and evaluation](https://mlsanity.com/paper/1911.10683/) (2019)
- [CascadeTabNet: An approach for end to end table detection and structure recognition from image-based documents](https://mlsanity.com/paper/2004.12629/) (2020)
- [LayoutParser: A Unified Toolkit for Deep Learning Based Document Image Analysis](https://mlsanity.com/paper/2103.15348/) (2021)
- [LGPMA: Complicated Table Structure Recognition with Local and Global Pyramid Mask Alignment](https://mlsanity.com/paper/2105.06224/) (2021)
- [DE-GAN: A Conditional Generative Adversarial Network for Document Enhancement](https://mlsanity.com/paper/2010.08764/) (2020)
- [DocTr: Document Image Transformer for Geometric Unwarping and Illumination Correction](https://mlsanity.com/paper/2110.12942/) (2021)
- [Document Rectification and Illumination Correction using a Patch-based CNN](https://mlsanity.com/paper/1909.09470/) (2019)
- [Multiview Rectification of Folded Documents](https://mlsanity.com/paper/1606.00166/) (2016)
- [DocEnTr: An End-to-End Document Image Enhancement Transformer](https://mlsanity.com/paper/2201.10252/) (2022)
- [A Large-Scale Comparison of Historical Text Normalization Systems](https://mlsanity.com/paper/1904.02036/) (2019)
- [Current Status and Performance Analysis of Table Recognition in Document Images with Deep Neural Networks](https://mlsanity.com/paper/2104.14272/) (2021)

## Built on by (most cited first, 1 of 1)

- [Predicting the Past: Estimating Historical Appraisals with OCR and Machine Learning](https://mlsanity.com/paper/2505.24676/) (2025)

## Abstract (arXiv)

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.