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

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

Auteurs : 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
Rubrique : Humanités numériques en langues
Publié le : 30 novembre 2022
Accepté le : 30 novembre 2022
Soumis le : 8 octobre 2021
Mots-clés : Computer Science - Computation and Language,Computer Science - Artificial Intelligence,Computer Science - Machine Learning,I.2.7

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