Combining neural networks for arabic handwriting recognition
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Date
2011
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Publisher
IEEE
Abstract
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.
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Keywords
Classifier system, Arabic recognition, Fuzzy ART network, RBF network, Hu momen, Zernike moment