InforLorV4, Hal, Corpus, bibRecord, 003B23

Pattern-Based Approach to Table Extraction

Identifieur interne : 003B23 ( Hal/Corpus ); précédent : 003B22; suivant : 003B24

Pattern-Based Approach to Table Extraction

Auteurs : Santosh K. C. ; Abdel Belaïd

Source :

RBID : Hal:hal-00788323

English descriptors

mix :
- Attributed Relational Graph, Graph Mining, Input Pattern, Table Extraction, Table Extraction..

Abstract

In this paper, we address a client-driven approach to automatically extract information content within the table in document images. We start with a graph-based representation of a set of key-fields selected by clients and perform graph mining in a document in order to learn them to produce a model. Such models are aimed to use to extract information content in the absence of clients. To avoid NP-hard general problem, our graph matching is based on relation assignment to see whether pairs of nodes are semantically identical. We have validated the concept by using a real-world industrial problem.

Url:

https://hal.inria.fr/hal-00788323

Links to Exploration step

Hal:hal-00788323

Le document en format XML

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<front><div type="abstract" xml:lang="en">In this paper, we address a client-driven approach to automatically extract information content within the table in document images. We start with a graph-based representation of a set of key-fields selected by clients and perform graph mining in a document in order to learn them to produce a model. Such models are aimed to use to extract information content in the absence of clients. To avoid NP-hard general problem, our graph matching is based on relation assignment to see whether pairs of nodes are semantically identical. We have validated the concept by using a real-world industrial problem.</div>
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<profileDesc><langUsage><language ident="en">English</language>
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<textClass><keywords scheme="author"><term xml:lang="en">Table Extraction</term>
<term xml:lang="en">Input Pattern</term>
<term xml:lang="en">Attributed Relational Graph</term>
<term xml:lang="en">Graph Mining</term>
<term xml:lang="en">Table Extraction.</term>
</keywords>
<classCode scheme="halDomain" n="info.info-cv">Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]</classCode>
<classCode scheme="halDomain" n="info.info-tt">Computer Science [cs]/Document and Text Processing</classCode>
<classCode scheme="halTypology" n="COMM">Conference papers</classCode>
</textClass>
<abstract xml:lang="en">In this paper, we address a client-driven approach to automatically extract information content within the table in document images. We start with a graph-based representation of a set of key-fields selected by clients and perform graph mining in a document in order to learn them to produce a model. Such models are aimed to use to extract information content in the absence of clients. To avoid NP-hard general problem, our graph matching is based on relation assignment to see whether pairs of nodes are semantically identical. We have validated the concept by using a real-world industrial problem.</abstract>
<particDesc><org type="consortium">ITESOFT</org>
</particDesc>
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Pattern-Based Approach to Table Extraction

Pattern-Based Approach to Table Extraction

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