Proposition to distinguish Machine-Printed from Handwritten Arabic and Latin Words
Identifieur interne : 000C50 ( Main/Exploration ); précédent : 000C49; suivant : 000C51Proposition to distinguish Machine-Printed from Handwritten Arabic and Latin Words
Auteurs : Asma Saïdani [Tunisie] ; Afef Kacem Echi [Tunisie] ; Abdel Belaïd [France]Source :
Descripteurs français
- Wicri :
- topic : Classification.
English descriptors
- mix :
Abstract
—In this work, we gathered some contributions to identify script and its nature. We successfully employed many features to distinguish between handwritten and machine-printed Arabic and Latin scripts at word level. Some of them are previously used in the literature, and the others are here proposed. The new proposed structural features are intrinsic to Arabic and Latin scripts. The performance of all extracted features is studied towards this paper. We also compared the performance of three classifiers: Bayes (AODEsr), k-Nearest Neighbor (k-NN) and Decision Tree (J48), used to identify the script at word level. These classifiers have been chosen enough different to test the feature contributions. We carried experiments using standard databases. Obtained results demonstrate used feature capability to capture differences between scripts. Using a set of 58 selected features and a Bayes-based classifier, we achieved an average identification rate equals to 98.72%, which considered a very satisfactory rate compared to some related works.
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Le document en format XML
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We successfully employed many features to distinguish between handwritten and machine-printed Arabic and Latin scripts at word level. Some of them are previously used in the literature, and the others are here proposed. The new proposed structural features are intrinsic to Arabic and Latin scripts. The performance of all extracted features is studied towards this paper. We also compared the performance of three classifiers: Bayes (AODEsr), k-Nearest Neighbor (k-NN) and Decision Tree (J48), used to identify the script at word level. These classifiers have been chosen enough different to test the feature contributions. We carried experiments using standard databases. Obtained results demonstrate used feature capability to capture differences between scripts. Using a set of 58 selected features and a Bayes-based classifier, we achieved an average identification rate equals to 98.72%, which considered a very satisfactory rate compared to some related works.</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 Echi, Afef" sort="Kacem Echi, Afef" uniqKey="Kacem Echi A" first="Afef" last="Kacem Echi">Afef Kacem Echi</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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