# Rerunning OCR: A Machine Learning Approach to Quality Assessment and Enhancement Prediction

arXiv: https://arxiv.org/abs/2110.01661 · HTML: https://mlsanity.com/paper/2110.01661/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 23 references that are in the map.
- First posted on arXiv 4 Oct 2021.

## Builds on (most cited first, 1 of 1)

- [Learning Surrogate Models of Document Image Quality Metrics for Automated Document Image Processing](https://mlsanity.com/paper/1712.03738/) (2017)

## Abstract (arXiv)

Iterating with new and improved OCR solutions enforces decision making when it comes to targeting the right candidates for reprocessing. This especially applies when the underlying data collection is of considerable size and rather diverse in terms of fonts, languages, periods of publication and consequently OCR quality. This article captures the efforts of the National Library of Luxembourg to support those targeting decisions. They are crucial in order to guarantee low computational overhead and reduced quality degradation risks, combined with a more quantifiable OCR improvement. In particular, this work explains the methodology of the library with respect to text block level quality assessment. Through extension of this technique, a regression model, that is able to take into account the enhancement potential of a new OCR engine, is also presented. They both mark promising approaches, especially for cultural institutions dealing with historical data of lower quality.