Building a test collection for complex document information processing
Identifieur interne : 001178 ( Main/Exploration ); précédent : 001177; suivant : 001179Building a test collection for complex document information processing
Auteurs : D. Lewis [États-Unis] ; G. Agam [États-Unis] ; S. Argamon [États-Unis] ; O. Frieder [États-Unis] ; D. Grossman [États-Unis] ; J. Heard [États-Unis]Source :
Descripteurs français
- Pascal (Inist)
- Recherche information, Traitement document, Traitement information, Accès information, Technologie information, Fouille donnée, Analyse donnée, Extraction information, Reconnaissance optique caractère, Reconnaissance caractère, Analyse documentaire, Système recherche, Structure document, Analyse signature, ..
English descriptors
- KwdEn :
Abstract
Research and development of information access technology for scanned paper documents has been hampered by the lack of public test collections of realistic scope and complexity. As part of a project to create a prototype system for search and mining of masses of document images, we are assembling a 1.5 terabyte dataset to support evaluation of both end-to-end complex document information processing (CDIP) tasks (e.g., text retrieval and data mining) as well as component technologies such as optical character recognition (OCR), document structure analysis, signature matching, and authorship attribution.
Affiliations:
Links toward previous steps (curation, corpus...)
- to stream PascalFrancis, to step Corpus: 000362
- to stream PascalFrancis, to step Curation: 000424
- to stream PascalFrancis, to step Checkpoint: 000348
- to stream Main, to step Merge: 001209
- to stream Main, to step Curation: 001178
Le document en format XML
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<front><div type="abstract" xml:lang="en">Research and development of information access technology for scanned paper documents has been hampered by the lack of public test collections of realistic scope and complexity. As part of a project to create a prototype system for search and mining of masses of document images, we are assembling a 1.5 terabyte dataset to support evaluation of both end-to-end complex document information processing (CDIP) tasks (e.g., text retrieval and data mining) as well as component technologies such as optical character recognition (OCR), document structure analysis, signature matching, and authorship attribution.</div>
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<tree><country name="États-Unis"><region name="Illinois"><name sortKey="Lewis, D" sort="Lewis, D" uniqKey="Lewis D" first="D." last="Lewis">D. Lewis</name>
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<name sortKey="Agam, G" sort="Agam, G" uniqKey="Agam G" first="G." last="Agam">G. Agam</name>
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<name sortKey="Frieder, O" sort="Frieder, O" uniqKey="Frieder O" first="O." last="Frieder">O. Frieder</name>
<name sortKey="Grossman, D" sort="Grossman, D" uniqKey="Grossman D" first="D." last="Grossman">D. Grossman</name>
<name sortKey="Heard, J" sort="Heard, J" uniqKey="Heard J" first="J." last="Heard">J. Heard</name>
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