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.

Combining Morphological and Histogram based Text Line Segmentation in the OCR Context

Text line segmentation is one of the pre-stages of modern optical character recognition systems. The algorithmic approach proposed by this paper has been designed for this exact purpose. Its main characteristic is the combination of two different techniques, morphological image operations and horizontal histogram projections. The method was developed to be applied on a historic data collection that commonly features quality issues, such as degraded paper, blurred text, or curved text lines. For that reason, the segmenter in question could be of particular interest for cultural institutions, such as libraries, archives, museums, ..., that want access to robust line bounding boxes for a given historic document. Because of the promising segmentation results that are joined by low computational cost, the algorithm was incorporated into the OCR pipeline of the National Library of Luxembourg, in the context of the initiative of reprocessing their historic newspaper collection. The general contribution of this paper is to outline the approach and to evaluate the gains in terms of accuracy and speed, comparing it to the segmentation algorithm bundled with the used open source OCR software.