Combining neural networks for Arabic handwriting recognition

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.