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OCR correction based on document level knowledge

Identifieur interne : 001782 ( Main/Exploration ); précédent : 001781; suivant : 001783

OCR correction based on document level knowledge

Auteurs : T. Nartker [États-Unis] ; K. Taghva [États-Unis] ; R. Young [États-Unis] ; J. Borsack [États-Unis] ; A. Condit [États-Unis]

Source :

SPIE proceedings series [ 1017-2653 ] ; 2003.

RBID : Pascal:03-0421337

Descripteurs français

Pascal (Inist)
- Reconnaissance optique caractère, Précision, Correction erreur, Traitement document, Conversion donnée, Système, Evaluation performance, MANICURE (système traitement document), Mot vide.

English descriptors

KwdEn :
- Accuracy, Data conversion, Document processing, Error correction, Optical character recognition, Performance evaluation, Stop word, System.

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 (Optical Character Recognition) 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 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.

Affiliations:

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to stream PascalFrancis, to step Corpus: 000596
to stream PascalFrancis, to step Curation: 000195
to stream PascalFrancis, to step Checkpoint: 000546
to stream Main, to step Merge: 001860
to stream Main, to step Curation: 001782

Le document en format XML

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<term>Performance evaluation</term>
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<front><div type="abstract" xml:lang="en">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 (Optical Character Recognition) 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 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.</div>
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OCR correction based on document level knowledge

OCR correction based on document level knowledge

Source :

Descripteurs français

English descriptors

Abstract

Links toward previous steps (curation, corpus...)

Le document en format XML

Pour manipuler ce document sous Unix (Dilib)

Pour mettre un lien sur cette page dans le réseau Wicri