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

Iterating with new and improved OCR solutions enforces decisions to be taken when it comes to targeting the right reprocessing candidates. 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 exact 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. As an extension of this technique, another contribution comes in the form of a regression model that takes the enhancement potential of a new OCR engine into account. They both mark promising approaches, especially for cultural institutions dealing with historic data of lower quality.