Arabic Handwritten Words Off-line Recognition based on HMMs and DBNs
Identifieur interne : 000F65 ( Hal/Corpus ); précédent : 000F64; suivant : 000F66Arabic Handwritten Words Off-line Recognition based on HMMs and DBNs
Auteurs : Akram Khémiri ; Afef Kacem ; Abdel Belaïd ; Mourad ElloumiSource :
Abstract
In this work, we investigate the combination of PGM (Propabilistic Graphical Models) classifiers, either independent or coupled, for the recognition of Arabic handwritten words. The independent classifiers are vertical and horizontal HMMs (Hidden Markov Models) whose observable outputs are features extracted from the image columns and the image rows respectively. The coupled classifiers associate the vertical and horizontal observation streams into a single DBN (Dynamic Bayesian Network). A novel method to extract word baseline and a simple and easily extractable features to construct feature vectors for words in the vocabulary are proposed. Some of these features are statistical, based on pixel distributions and local pixel configurations. Others are structural, based on the presence of ascenders, descenders, loops and diacritic points. Experiments on handwritten Arabic words from IFN/ENIT strongly support the feasibility of the proposed approach. The recognition rates achieve 90.42% with vertical and horizontal HMM, 85.03% and 85.21% with respectively a first and a second DBN which outperform results of some works based on PGMs.
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DOI: 10.1109/ICDAR.2015.7333724
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The independent classifiers are vertical and horizontal HMMs (Hidden Markov Models) whose observable outputs are features extracted from the image columns and the image rows respectively. The coupled classifiers associate the vertical and horizontal observation streams into a single DBN (Dynamic Bayesian Network). A novel method to extract word baseline and a simple and easily extractable features to construct feature vectors for words in the vocabulary are proposed. Some of these features are statistical, based on pixel distributions and local pixel configurations. Others are structural, based on the presence of ascenders, descenders, loops and diacritic points. Experiments on handwritten Arabic words from IFN/ENIT strongly support the feasibility of the proposed approach. The recognition rates achieve 90.42% with vertical and horizontal HMM, 85.03% and 85.21% with respectively a first and a second DBN which outperform results of some works based on PGMs.</div></front></TEI><hal api="V3"><titleStmt><title xml:lang="en">Arabic Handwritten Words Off-line Recognition based on HMMs and DBNs</title><author role="aut"><persName><forename type="first">Akram</forename><surname>Khémiri</surname></persName><email></email><idno type="halauthor">1282883</idno><affiliation ref="#struct-39219"></affiliation></author><author role="aut"><persName><forename type="first">Afef</forename><surname>Kacem</surname></persName><email></email><idno type="halauthor">373793</idno><affiliation ref="#struct-39219"></affiliation></author><author role="aut"><persName><forename type="first">Abdel</forename><surname>Belaïd</surname></persName><email>abelaid@loria.fr</email><ptr type="url" target="http://www.loria.fr/~abelaid/"></ptr><idno type="halauthor">1282884</idno><affiliation ref="#struct-206042"></affiliation><affiliation ref="#struct-206040"></affiliation></author><author role="aut"><persName><forename type="first">Mourad</forename><surname>Elloumi</surname></persName><email>mourad.elloumi@gmail.com</email><idno type="halauthor">676275</idno><affiliation ref="#struct-442209"></affiliation></author><editor role="depositor"><persName><forename>Abdel</forename><surname>Belaid</surname></persName><email>abelaid@loria.fr</email></editor></titleStmt><editionStmt><edition n="v1" type="current"><date type="whenSubmitted">2016-01-25 12:46:22</date><date type="whenModified">2016-01-29 01:06:45</date><date type="whenReleased">2016-01-28 11:10:50</date><date type="whenProduced">2015-08-23</date><date type="whenEndEmbargoed">2016-01-25</date><ref type="file" target="https://hal.inria.fr/hal-01254724/document"><date notBefore="2016-01-25"></date></ref><ref type="file" subtype="author" n="1" target="https://hal.inria.fr/hal-01254724/file/ICDAR-AKRAM-AFEF-ABDEL.pdf"><date notBefore="2016-01-25"></date></ref></edition><respStmt><resp>contributor</resp><name key="113588"><persName><forename>Abdel</forename><surname>Belaid</surname></persName><email>abelaid@loria.fr</email></name></respStmt></editionStmt><publicationStmt><distributor>CCSD</distributor><idno type="halId">hal-01254724</idno><idno type="halUri">https://hal.inria.fr/hal-01254724</idno><idno type="halBibtex">khemiri:hal-01254724</idno><idno type="halRefHtml">ICDAR 2015 - 13th International Conference on Document Analysis and Recognition, Aug 2015, Nancy, France. pp.51 - 55, 2015, <10.1109/ICDAR.2015.7333724></idno><idno type="halRef">ICDAR 2015 - 13th International Conference on Document Analysis and Recognition, Aug 2015, Nancy, France. pp.51 - 55, 2015, <10.1109/ICDAR.2015.7333724></idno></publicationStmt><seriesStmt><idno type="stamp" n="CNRS">CNRS - Centre national de la recherche scientifique</idno><idno type="stamp" n="LORIA-TALC" p="LORIA">Traitement automatique des langues et des connaissances</idno><idno type="stamp" n="INRIA">INRIA - Institut National de Recherche en Informatique et en Automatique</idno><idno type="stamp" n="LORIA">LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications</idno><idno type="stamp" n="UNIV-LORRAINE">Université de Lorraine</idno><idno type="stamp" n="LORIA2">Publications du LORIA</idno></seriesStmt><notesStmt><note type="audience" n="2">International</note><note type="invited" n="0">No</note><note type="popular" n="0">No</note><note type="peer" n="1">Yes</note><note type="proceedings" n="0">No</note></notesStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Arabic Handwritten Words Off-line Recognition based on HMMs and DBNs</title><author role="aut"><persName><forename type="first">Akram</forename><surname>Khémiri</surname></persName><idno type="halAuthorId">1282883</idno><affiliation ref="#struct-39219"></affiliation></author><author role="aut"><persName><forename type="first">Afef</forename><surname>Kacem</surname></persName><idno type="halAuthorId">373793</idno><affiliation ref="#struct-39219"></affiliation></author><author role="aut"><persName><forename type="first">Abdel</forename><surname>Belaïd</surname></persName><email>abelaid@loria.fr</email><ptr type="url" target="http://www.loria.fr/~abelaid/"></ptr><idno type="halAuthorId">1282884</idno><affiliation ref="#struct-206042"></affiliation><affiliation ref="#struct-206040"></affiliation></author><author role="aut"><persName><forename type="first">Mourad</forename><surname>Elloumi</surname></persName><email>mourad.elloumi@gmail.com</email><idno type="halAuthorId">676275</idno><affiliation ref="#struct-442209"></affiliation></author></analytic><monogr><meeting><title>ICDAR 2015 - 13th International Conference on Document Analysis and Recognition</title><date type="start">2015-08-23</date><date type="end">2015-08-26</date><settlement>Nancy</settlement><country key="FR">France</country></meeting><imprint><biblScope unit="pp">51 - 55</biblScope><date type="datePub">2015</date></imprint></monogr><idno type="doi">10.1109/ICDAR.2015.7333724</idno></biblStruct></sourceDesc><profileDesc><langUsage><language ident="en">English</language></langUsage><textClass><classCode scheme="halDomain" n="info">Computer Science [cs]</classCode><classCode scheme="halTypology" n="COMM">Conference papers</classCode></textClass><abstract xml:lang="en">In this work, we investigate the combination of PGM (Propabilistic Graphical Models) classifiers, either independent or coupled, for the recognition of Arabic handwritten words. The independent classifiers are vertical and horizontal HMMs (Hidden Markov Models) whose observable outputs are features extracted from the image columns and the image rows respectively. The coupled classifiers associate the vertical and horizontal observation streams into a single DBN (Dynamic Bayesian Network). A novel method to extract word baseline and a simple and easily extractable features to construct feature vectors for words in the vocabulary are proposed. Some of these features are statistical, based on pixel distributions and local pixel configurations. Others are structural, based on the presence of ascenders, descenders, loops and diacritic points. Experiments on handwritten Arabic words from IFN/ENIT strongly support the feasibility of the proposed approach. The recognition rates achieve 90.42% with vertical and horizontal HMM, 85.03% and 85.21% with respectively a first and a second DBN which outperform results of some works based on PGMs.</abstract></profileDesc></hal></record>Pour manipuler ce document sous Unix (Dilib)
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