A Progressive Learning Method for Symbol Recognition
Identifieur interne : 000407 ( Hal/Corpus ); précédent : 000406; suivant : 000408A Progressive Learning Method for Symbol Recognition
Auteurs : Sabine Barrat ; Salvatore TabboneSource :
Abstract
This paper deals with a progressive learning method for symbol recognition which improves its own recognition rate when new symbols are recognized in graphic documents. We propose a discriminant analysis method which provides allocation rules from a training set of labelled data. However a discriminant analysis method is efficient only if the training set and the test data are defined in the same conditions but it is rare in real life. In order to overcome this problem, a conditional vector is added to each instance to take into account the parasitic effects between the test data and the training set. We also propose an adaptation to consider the user feedback.
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We propose a discriminant analysis method which provides allocation rules from a training set of labelled data. However a discriminant analysis method is efficient only if the training set and the test data are defined in the same conditions but it is rare in real life. In order to overcome this problem, a conditional vector is added to each instance to take into account the parasitic effects between the test data and the training set. We also propose an adaptation to consider the user feedback.</div></front></TEI><hal api="V3"><titleStmt><title xml:lang="en">A Progressive Learning Method for Symbol Recognition</title><author role="aut"><persName><forename type="first">Sabine</forename><surname>Barrat</surname></persName><email>sabine.barrat@loria.fr</email><idno type="halauthor">150074</idno><orgName ref="#struct-300292"></orgName><affiliation ref="#struct-2361"></affiliation></author><author role="aut"><persName><forename type="first">Salvatore</forename><surname>Tabbone</surname></persName><email>tabbone@loria.fr</email><idno type="halauthor">150075</idno><orgName ref="#struct-300292"></orgName><affiliation ref="#struct-2361"></affiliation></author><editor role="depositor"><persName><forename>Sabine</forename><surname>Barrat</surname></persName><email>sabine.barrat@univ-tours.fr</email></editor></titleStmt><editionStmt><edition n="v1" type="current"><date type="whenSubmitted">2008-11-04 15:37:41</date><date type="whenModified">2016-05-19 01:09:17</date><date type="whenReleased">2008-11-04 16:23:22</date><date type="whenProduced">2008-01-28</date><date type="whenEndEmbargoed">2008-11-04</date><ref type="file" target="https://hal.inria.fr/inria-00339115/document"><date notBefore="2008-11-04"></date></ref><ref type="file" subtype="author" n="1" target="https://hal.inria.fr/inria-00339115/file/jucs_14_2_0224_0236_barrat.pdf"><date notBefore="2008-11-04"></date></ref></edition><respStmt><resp>contributor</resp><name key="114970"><persName><forename>Sabine</forename><surname>Barrat</surname></persName><email>sabine.barrat@univ-tours.fr</email></name></respStmt></editionStmt><publicationStmt><distributor>CCSD</distributor><idno type="halId">inria-00339115</idno><idno type="halUri">https://hal.inria.fr/inria-00339115</idno><idno type="halBibtex">barrat:inria-00339115</idno><idno type="halRefHtml">Journal of Universal Computer Science, Springer, 2008, 14 (2), pp.224-336</idno><idno type="halRef">Journal of Universal Computer Science, Springer, 2008, 14 (2), pp.224-336</idno></publicationStmt><seriesStmt><idno type="stamp" n="CNRS">CNRS - Centre national de la recherche scientifique</idno><idno type="stamp" n="INRIA">INRIA - Institut National de Recherche en Informatique et en Automatique</idno><idno type="stamp" n="INPL">Institut National Polytechnique de Lorraine</idno><idno type="stamp" n="LORIA2">Publications du LORIA</idno><idno type="stamp" n="INRIA-NANCY-GRAND-EST">INRIA Nancy - Grand Est</idno><idno type="stamp" n="LORIA">LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications</idno><idno type="stamp" n="LORIA-TALC" p="LORIA">Traitement automatique des langues et des connaissances</idno><idno type="stamp" n="UNIV-LORRAINE">Université de Lorraine</idno><idno type="stamp" n="INRIA-LORRAINE">INRIA Nancy - Grand Est</idno><idno type="stamp" n="LABO-LORIA-SET" p="LORIA">LABO-LORIA-SET</idno></seriesStmt><notesStmt><note type="audience" n="2">International</note><note type="popular" n="0">No</note><note type="peer" n="1">Yes</note></notesStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">A Progressive Learning Method for Symbol Recognition</title><author role="aut"><persName><forename type="first">Sabine</forename><surname>Barrat</surname></persName><email>sabine.barrat@loria.fr</email><idno type="halAuthorId">150074</idno><orgName ref="#struct-300292"></orgName><affiliation ref="#struct-2361"></affiliation></author><author role="aut"><persName><forename type="first">Salvatore</forename><surname>Tabbone</surname></persName><email>tabbone@loria.fr</email><idno type="halAuthorId">150075</idno><orgName ref="#struct-300292"></orgName><affiliation ref="#struct-2361"></affiliation></author></analytic><monogr><idno type="halJournalId" status="VALID">20954</idno><title level="j">Journal of Universal Computer Science</title><imprint><publisher>Springer</publisher><biblScope unit="volume">14</biblScope><biblScope unit="issue">2</biblScope><biblScope unit="pp">224-336</biblScope><date type="datePub">2008-01-28</date></imprint></monogr></biblStruct></sourceDesc><profileDesc><langUsage><language ident="en">English</language></langUsage><textClass><keywords scheme="author"><term xml:lang="it">conditional discriminant analysis</term><term xml:lang="it">symbol recognition</term></keywords><classCode scheme="halDomain" n="info.info-tt">Computer Science [cs]/Document and Text Processing</classCode><classCode scheme="halTypology" n="ART">Journal articles</classCode></textClass><abstract xml:lang="en">This paper deals with a progressive learning method for symbol recognition which improves its own recognition rate when new symbols are recognized in graphic documents. We propose a discriminant analysis method which provides allocation rules from a training set of labelled data. However a discriminant analysis method is efficient only if the training set and the test data are defined in the same conditions but it is rare in real life. In order to overcome this problem, a conditional vector is added to each instance to take into account the parasitic effects between the test data and the training set. We also propose an adaptation to consider the user feedback.</abstract></profileDesc></hal></record>Pour manipuler ce document sous Unix (Dilib)
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