A serial combination of neural network for arabic OCR

Abstract

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. © 2014 Springer International Publishing.