Learning to Detect Tables in Scanned Document Images using Line Information
Identifieur interne : 000076 ( Hal/Corpus ); précédent : 000075; suivant : 000077Learning to Detect Tables in Scanned Document Images using Line Information
Auteurs : Thotreingam Kasar ; Philippine Barlas ; Sebastien Adam ; Clément Chatelain ; Thierry PaquetSource :
English descriptors
Abstract
This paper presents a method to detect table regions in document images by identifying the column and row line separators and their properties. The method employs a runlength approach to identify the horizontal and vertical lines present in the input image. From each group of intersecting horizontal and vertical lines, a set of 26 low-level features are extracted and an SVM classifier is used to test if it belongs to a table or not. The performance of the method is evaluated on a heterogeneous corpus of French, English and Arabic documents that contain various types of table structures and compared with that of the Tesseract OCR system.
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Hal:hal-00905546Le document en format XML
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<front><div type="abstract" xml:lang="en">This paper presents a method to detect table regions in document images by identifying the column and row line separators and their properties. The method employs a runlength approach to identify the horizontal and vertical lines present in the input image. From each group of intersecting horizontal and vertical lines, a set of 26 low-level features are extracted and an SVM classifier is used to test if it belongs to a table or not. The performance of the method is evaluated on a heterogeneous corpus of French, English and Arabic documents that contain various types of table structures and compared with that of the Tesseract OCR system.</div>
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<hal api="V3"><titleStmt><title xml:lang="en">Learning to Detect Tables in Scanned Document Images using Line Information</title>
<author role="aut"><persName><forename type="first">Thotreingam</forename>
<surname>Kasar</surname>
</persName>
<email></email>
<idno type="halauthor">944933</idno>
<affiliation ref="#struct-23832"></affiliation>
</author>
<author role="aut"><persName><forename type="first">Philippine</forename>
<surname>Barlas</surname>
</persName>
<email></email>
<idno type="halauthor">944934</idno>
<affiliation ref="#struct-23832"></affiliation>
</author>
<author role="aut"><persName><forename type="first">Sebastien</forename>
<surname>Adam</surname>
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<email></email>
<idno type="halauthor">167931</idno>
<affiliation ref="#struct-23832"></affiliation>
</author>
<author role="aut"><persName><forename type="first">Clément</forename>
<surname>Chatelain</surname>
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<email>clement.chatelain@univ-rouen.fr</email>
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<idno type="halauthor">134991</idno>
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<author role="aut"><persName><forename type="first">Thierry</forename>
<surname>Paquet</surname>
</persName>
<email></email>
<idno type="halauthor">86898</idno>
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<editor role="depositor"><persName><forename>Clément</forename>
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<email>clement.chatelain@univ-rouen.fr</email>
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<editionStmt><edition n="v1" type="current"><date type="whenSubmitted">2013-11-19 11:33:50</date>
<date type="whenModified">2015-05-19 16:36:11</date>
<date type="whenReleased">2013-11-21 09:31:37</date>
<date type="whenProduced">2013</date>
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<seriesStmt><idno type="stamp" n="UNIV-LEHAVRE">Université du Havre</idno>
<idno type="stamp" n="UNIV-ROUEN">Université de Rouen</idno>
<idno type="stamp" n="LITIS">Laboratoire d'Informatique, de Traitement de l'Information et des Systèmes</idno>
<idno type="stamp" n="COMUE-NORMANDIE">Normandie Université</idno>
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<note type="popular" n="0">No</note>
<note type="peer" n="1">Yes</note>
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<sourceDesc><biblStruct><analytic><title xml:lang="en">Learning to Detect Tables in Scanned Document Images using Line Information</title>
<author role="aut"><persName><forename type="first">Thotreingam</forename>
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<author role="aut"><persName><forename type="first">Sebastien</forename>
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<author role="aut"><persName><forename type="first">Clément</forename>
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<author role="aut"><persName><forename type="first">Thierry</forename>
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<idno type="halAuthorId">86898</idno>
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<monogr><title level="m">ICDAR</title>
<meeting><title>ICDAR</title>
<date type="start">2013</date>
<country key="FR">France</country>
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<profileDesc><langUsage><language ident="en">English</language>
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<abstract xml:lang="en">This paper presents a method to detect table regions in document images by identifying the column and row line separators and their properties. The method employs a runlength approach to identify the horizontal and vertical lines present in the input image. From each group of intersecting horizontal and vertical lines, a set of 26 low-level features are extracted and an SVM classifier is used to test if it belongs to a table or not. The performance of the method is evaluated on a heterogeneous corpus of French, English and Arabic documents that contain various types of table structures and compared with that of the Tesseract OCR system.</abstract>
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