Arabic/Latin and Machine-printed/Handwritten Word Discrimination using HOG-based Shape Descriptor
Identifieur interne : 000394 ( Main/Exploration ); précédent : 000393; suivant : 000395Arabic/Latin and Machine-printed/Handwritten Word Discrimination using HOG-based Shape Descriptor
Auteurs : Asma Saïdani [Tunisie] ; Afef Kacem [Tunisie] ; Belaïd Abdel [France]Source :
English descriptors
Abstract
In this paper, we present an approach for Arabic and Latin script and its type identification based onHistogram of Oriented Gradients (HOG) descriptors. HOGs are first applied at word level based on writingorientation analysis. Then, they are extended to word image partitions to capture fine and discriminativedetails. Pyramid HOG are also used to study their effects on different observation levels of the image.Finally, co-occurrence matrices of HOG are performed to consider spatial information between pairs ofpixels which is not taken into account in basic HOG. A genetic algorithm is applied to select the potentialinformative features combinations which maximizes the classification accuracy. The output is a relativelyshort descriptor that provides an effective input to a Bayes-based classifier. Experimental results on a set ofwords, extracted from standard databases, show that our identification system is robust and provides goodword script and type identification: 99.07% of words are correctly classified.
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DOI: 10.5565/rev/elcvia.762
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HOGs are first applied at word level based on writingorientation analysis. Then, they are extended to word image partitions to capture fine and discriminativedetails. Pyramid HOG are also used to study their effects on different observation levels of the image.Finally, co-occurrence matrices of HOG are performed to consider spatial information between pairs ofpixels which is not taken into account in basic HOG. A genetic algorithm is applied to select the potentialinformative features combinations which maximizes the classification accuracy. The output is a relativelyshort descriptor that provides an effective input to a Bayes-based classifier. Experimental results on a set ofwords, extracted from standard databases, show that our identification system is robust and provides goodword script and type identification: 99.07% of words are correctly classified.</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="Saidani, Asma" sort="Saidani, Asma" uniqKey="Saidani A" first="Asma" last="Saïdani">Asma Saïdani</name></noRegion><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="Abdel, Belaid" sort="Abdel, Belaid" uniqKey="Abdel B" first="Belaïd" last="Abdel">Belaïd Abdel</name></region></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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