Arabic Handwritten Words Off-line Recognition based on HMMs and DBNs
Identifieur interne : 000388 ( Main/Exploration ); précédent : 000387; suivant : 000389Arabic Handwritten Words Off-line Recognition based on HMMs and DBNs
Auteurs : Akram Khémiri [Tunisie] ; Afef Kacem [Tunisie] ; Abdel Belaïd [France] ; Mourad Elloumi [Tunisie]Source :
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
Affiliations:
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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><affiliations><list><country><li>France</li><li>Tunisie</li></country><region><li>Grand Est</li><li>Lorraine (région)</li></region><settlement><li>Metz</li><li>Nancy</li></settlement><orgName><li>Université de Lorraine</li></orgName></list><tree><country name="Tunisie"><noRegion><name sortKey="Khemiri, Akram" sort="Khemiri, Akram" uniqKey="Khemiri A" first="Akram" last="Khémiri">Akram Khémiri</name></noRegion><name sortKey="Elloumi, Mourad" sort="Elloumi, Mourad" uniqKey="Elloumi M" first="Mourad" last="Elloumi">Mourad Elloumi</name><name sortKey="Kacem, Afef" sort="Kacem, Afef" uniqKey="Kacem A" first="Afef" last="Kacem">Afef Kacem</name></country><country name="France"><region name="Grand Est"><name sortKey="Belaid, Abdel" sort="Belaid, Abdel" uniqKey="Belaid A" first="Abdel" last="Belaïd">Abdel Belaïd</name></region></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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