A learning framework for the optimization and automation of document binarization methods
Computer Vision and Image Understanding
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Document enhancement tools are a valuable help in the study of historic documents. Given proper filter settings, many effects that impair the legibility can be evened out (e.g. washed out ink, stained and yellowed paper). However, because of differing authors, languages, handwritings, fonts and paper conditions, no single filter parameter set fits all documents. Therefore, the parameters are usually tuned in a time-consuming manual process to every individual document. To simplify this procedure, this paper introduces a classifier for the legibility of an enhanced historic text document. Experiments on the binarisation of a set of documents from 1938 to 1946 show that the classifier can be used to automatically derive robust filter settings for a variety of documents.