Recognition of Arabic Characters using Spiking Neural Networks

Text Recognition is one of the active and challenging areas of research in the pattern recognition field. It covers many applications like automatic number plate recognition, bank cheques, aid of reading for blind, and hand-written document conversion into form of structural text. In the present work, the spiking neural network (SNN) model is utilized for the identification of Arabic characters in a character set. The structure of considered neural network consists of two layers with Izhekevich neurons. Remote Supervised Method (ReSuMe) has been used as a learning rule for training. Also, the recognition of Arabic characters has been considered based on spike Neural Networks. The network could successfully recognize character set consisting of 28 isolating Arabic characters.