OCR correction based on document level knowledge

For over 10 years, the Information Science Research Institute (ISRI) at UNLV has worked on problems associated with the electronic conversion of archival document collections. Such collections typically have a large fraction of poor quality images and present a special challenge to OCR systems. Frequently, because of the size of the collection, manual correction of the output is not affordable. Because the output text is used only to build the index for an information retrieval (IR) system, the accuracy of non-stopwords is the most important measure of output quality. For these reasons, ISRI has focused on using document level knowledge as the best means of providing automatic correction of nonstopwords in OCR output. In 1998, we developed the MANICURE [1] post-processing system that combined several document level corrections. Because of the high cost of obtaining accurate ground-truth text at the document level, we have never been able to quantify the accuracy improvement achievable using document level knowledge. In this report, we describe an experiment to measure the actual number (and percentage) of non-stopwords corrected by the MANICURE system. We believe this to be the first quantitative measure of OCR conversion improvement that is possible using document level knowledge.