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Proceedings Paper

OCR correction based on document level knowledge
Author(s): Thomas A. Nartker; Kazem Taghva; Ron Young; Julie Borsack; Allen Condit
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Paper Abstract

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 non-stopwords 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.

Paper Details

Date Published: 13 January 2003
PDF: 8 pages
Proc. SPIE 5010, Document Recognition and Retrieval X, (13 January 2003); doi: 10.1117/12.479681
Show Author Affiliations
Thomas A. Nartker, Univ. of Nevada/Las Vegas (United States)
Kazem Taghva, Univ. of Nevada/Las Vegas (United States)
Ron Young, Univ. of Nevada/Las Vegas (United States)
Julie Borsack, Univ. of Nevada/Las Vegas (United States)
Allen Condit, Univ. of Nevada/Las Vegas (United States)

Published in SPIE Proceedings Vol. 5010:
Document Recognition and Retrieval X
Tapas Kanungo; Elisa H. Barney Smith; Jianying Hu; Paul B. Kantor, Editor(s)

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