Combining neural networks for arabic handwriting recognition

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dc.contributor.authorChergui, Leila
dc.contributor.authorKef, Maâmar
dc.contributor.authorChikhi, Salim
dc.date.accessioned2022-04-27T04:41:49Z
dc.date.available2022-04-27T04:41:49Z
dc.date.issued2011
dc.description.abstractCombining classifiers is an approach that has been shown to be useful on numerous occasions when striving for further improvement over the performance of individual classifiers. In this paper we present an off-line Multiple Classifier System (MCS) for Arabic handwriting recognition. The MCS combine two individual recognition systems based on Fuzzy ART network used for the first time in Arabic OCR, and Radial Basis Functions. We use various feature sets based on Hu and Zernike Invariant moments. For deriving the final decision, different combining schemes are applied. The best combination ensemble has a recognition rate of 90,1 %, which is significantly higher than the 84,31% achieved by the best individual classifier. To demonstrate the high performance of the classification system, the results are compared with three research using IFN/ENIT database.ar
dc.identifier.isbn978-1-4577-0908-1/11
dc.identifier.urihttp://hdl.handle.net/123456789/12994
dc.language.isoenar
dc.publisherIEEEar
dc.subjectClassifier systemar
dc.subjectArabic recognitionar
dc.subjectFuzzy ART networkar
dc.subjectRBF networkar
dc.subjectHu momenar
dc.subjectZernike momentar
dc.titleCombining neural networks for arabic handwriting recognitionar
dc.typeArticlear
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