An evaluation of an automatic markup system
Identifieur interne : 000A34 ( PascalFrancis/Checkpoint ); précédent : 000A33; suivant : 000A35An evaluation of an automatic markup system
Auteurs : K. Taghva [États-Unis] ; A. Condit [États-Unis] ; J. Borsack [États-Unis]Source :
- SPIE proceedings series [ 1017-2653 ] ; 1995.
Descripteurs français
- Pascal (Inist)
English descriptors
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Abstract
One predominant application of OCR is the recognition of full text documents for information retrieval. Modern retrieval systems exploit both the textual content of the document as well as its structure. The relationship between textual content and character accuracy have been the focus of recent studies. It has been shown that due to the redundancies in text, average precision and recall is not heavily affected by OCR character errors. What is not fully known is to what extent OCR devices can provide reliable information that can be used to capture the structure of the document. In this paper, we present a preliminary report on the design and evaluation of a system to automatically markup technical documents, based on information provided by an OCR device. The device we use differs from traditional OCR devices in that it not only performs optical character recognition, but also provides detailed information about page layout, word geometry, and font usage. Our automatic markup program, which we call Autotag, uses this information, combined with dictionary lookup and content analysis, to identify structural components of the text. These include the document title, author information, abstract, sections, section titles, paragraphs, sentences, and de-hyphenated words. A visual examination of the hardcopy will be compared to the output of our markup system to determine its correctness.
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Modern retrieval systems exploit both the textual content of the document as well as its structure. The relationship between textual content and character accuracy have been the focus of recent studies. It has been shown that due to the redundancies in text, average precision and recall is not heavily affected by OCR character errors. What is not fully known is to what extent OCR devices can provide reliable information that can be used to capture the structure of the document. In this paper, we present a preliminary report on the design and evaluation of a system to automatically markup technical documents, based on information provided by an OCR device. The device we use differs from traditional OCR devices in that it not only performs optical character recognition, but also provides detailed information about page layout, word geometry, and font usage. Our automatic markup program, which we call Autotag, uses this information, combined with dictionary lookup and content analysis, to identify structural components of the text. These include the document title, author information, abstract, sections, section titles, paragraphs, sentences, and de-hyphenated words. 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All rights reserved.</s1></fA44><fA45><s0>19 ref.</s0></fA45><fA47 i1="01" i2="1"><s0>97-0124556</s0></fA47><fA60><s1>P</s1><s2>C</s2></fA60><fA61><s0>A</s0></fA61><fA64 i1="01" i2="1"><s0>SPIE proceedings series</s0></fA64><fA66 i1="01"><s0>USA</s0></fA66><fC01 i1="01" l="ENG"><s0>One predominant application of OCR is the recognition of full text documents for information retrieval. Modern retrieval systems exploit both the textual content of the document as well as its structure. The relationship between textual content and character accuracy have been the focus of recent studies. It has been shown that due to the redundancies in text, average precision and recall is not heavily affected by OCR character errors. What is not fully known is to what extent OCR devices can provide reliable information that can be used to capture the structure of the document. In this paper, we present a preliminary report on the design and evaluation of a system to automatically markup technical documents, based on information provided by an OCR device. The device we use differs from traditional OCR devices in that it not only performs optical character recognition, but also provides detailed information about page layout, word geometry, and font usage. Our automatic markup program, which we call Autotag, uses this information, combined with dictionary lookup and content analysis, to identify structural components of the text. These include the document title, author information, abstract, sections, section titles, paragraphs, sentences, and de-hyphenated words. 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Taghva</name></region><name sortKey="Borsack, J" sort="Borsack, J" uniqKey="Borsack J" first="J." last="Borsack">J. Borsack</name><name sortKey="Condit, A" sort="Condit, A" uniqKey="Condit A" first="A." last="Condit">A. Condit</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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