مقـــــــــالات 2010-2015

Permanent URI for this collection

http://localhost:4000/handle/123456789/12894

Browse

Browsing مقـــــــــالات 2010-2015 by Subject "Arabic recognition"

Now showing 1 - 2 of 2
  • No Thumbnail Available
    Item
    A Serial combination of neural network for arabic OCR
    (Springer, 2014) Chergui, Leila; Kef, Maamar
    Today, handwriting recognition is one of the most challenging tasks and exciting areas of search in computer science. Indeed, despite the growing interest in this field, no satisfactory solution is available. For this reason Multiple Classifier Systems (MCS) based on the combination of outputs of a set of different classifiers have been proposed as a method for the developing of high performance classifier system. In this paper we describe a serial combination scheme of an Arabic Optical Character Recognition System. The classification engine is based on Adaptive Resonance Theory and Radial Basic Function, where an RBF network acting as the first classifier is properly combined with a set of ART1 network (one for each group) trained to classify the word image. The experiments applied on the IFN/ENIT database show that the proposed architecture exhibits best performance
  • No Thumbnail Available
    Item
    Combining neural networks for arabic handwriting recognition
    (IEEE, 2011) Chergui, Leila; Kef, Maâmar; Chikhi, Salim
    Combining 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.