Pit Schneider ; Yves Maurer - Rerunning OCR: A Machine Learning Approach to Quality Assessment and Enhancement Prediction

jdmdh:8561 - Journal of Data Mining & Digital Humanities, November 30, 2022, 2022 - https://doi.org/10.46298/jdmdh.8561
Rerunning OCR: A Machine Learning Approach to Quality Assessment and Enhancement PredictionArticle

Authors: Pit Schneider ORCID1; Yves Maurer ORCID1

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.


Volume: 2022
Section: Digital humanities in languages
Published on: November 30, 2022
Accepted on: November 30, 2022
Submitted on: October 8, 2021
Keywords: Computer Science - Computation and Language,Computer Science - Artificial Intelligence,Computer Science - Machine Learning,I.2.7

Consultation statistics

This page has been seen 2149 times.
This article's PDF has been downloaded 385 times.