Paper Info
Title
Ocr Correction Based On Document Level Knowledge
Abstract
dFor 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.
Year
DOI
Venue
2003
10.1117/12.479681
DOCUMENT RECOGNITION AND RETRIEVAL X
Keywords
Field
DocType
OCR correction, non-stopword accuracy, retrieval from noisy documents
Data mining,Information retrieval,Pattern recognition,Computer science,Document processing,Information science,Optical character recognition,Error detection and correction,Data conversion,Artificial intelligence,Stop words
Conference
Volume
ISSN
Citations 
5010
0277-786X
5
PageRank 
References 
Authors
0.63
3
5
Name
Order
Citations
PageRank
Thomas A. Nartker115321.50
Kazem Taghva235043.51
Ron Young3162.41
Julie Borsack420822.53
Allen Condit521022.95