Farsi handwritten recognition using combining neural networks based on stacked generalization

Abstract

Stack Generalization is a general method for combining low-level classifiers to achieve high-level classifier for impetrate to higher recognition rate. This paper proposed method based on Stack Generalization that named Modified Stack Generalization. In rour proposed model, unlike the conventional stacked generalization, the combiner receives the output of base classifiers and original input directly. The experiments have been done on 780 samples of 30 city names of Iran that for different experiments different number of training and testing samples was chosen. In the feature extraction Stage Gradient, Zoning methods are used, and also other method base on Gradient is suggested. Results show that Modified Stack generalization method with the recommended feature extraction method has been achieved to 92.21% recognition rate. Furthermore, Comparison test with other combination methods indicates that the proposed method yields improved recognition rate in the Farsi handwritten word recognition.