# A Tool for Facilitating OCR Postediting in Historical Documents

arXiv: https://arxiv.org/abs/2004.11471 · HTML: https://mlsanity.com/paper/2004.11471/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Apr 2020.

## Built on by (most cited first, 4 of 4)

- [Making Old Kurdish Publications Processable by Augmenting Available Optical Character Recognition Engines](https://mlsanity.com/paper/2404.06101/) (2024)
- [Comparative analysis of optical character recognition methods for S'ami texts from the National Library of Norway](https://mlsanity.com/paper/2501.07300/) (2025)
- [Sociotechnical Effects of Machine Translation](https://mlsanity.com/paper/2503.20959/) (2025)
- [Efficient Domain Adaptation for Text Line Recognition via Decoupled Language Models](https://mlsanity.com/paper/2603.28028/) (2026)

## Abstract (arXiv)

Optical character recognition (OCR) for historical documents is a complex procedure subject to a unique set of material issues, including inconsistencies in typefaces and low quality scanning. Consequently, even the most sophisticated OCR engines produce errors. This paper reports on a tool built for postediting the output of Tesseract, more specifically for correcting common errors in digitized historical documents. The proposed tool suggests alternatives for word forms not found in a specified vocabulary. The assumed error is replaced by a presumably correct alternative in the post-edition based on the scores of a Language Model (LM). The tool is tested on a chapter of the book An Essay Towards Regulating the Trade and Employing the Poor of this Kingdom (Cary ,1719). As demonstrated below, the tool is successful in correcting a number of common errors. If sometimes unreliable, it is also transparent and subject to human intervention.