Recognition of English Characters Using Spiking Neural Networks

Abstract

Abstract-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 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 English characters has been considered based on spike Neural Networks. The network could successfully recognize a set of 48 characters. Keyword-Spiking Neural Network (SNN), Remote Supervised Method (ReSuMe), Artificial Neural Networks(ANN) I. INTRODUCTIONSpiking neuron models lead to the rise of third generation of artificial neural networks(ANN) [1]. Such models add more realistic sense in neural simulation and invocation of the time concept. It has been shown that a considerable enhancement in image recognition speed can be reached by using pulse coding (versus rate coding) in biological neurons of the cortex [2]. Generally, a longer processing time is required by rate coding as the neuron could be fired after waiting for several spikes to come. Also, in case of pulse coding, the information based on timing order of incoming spikes can be encoded at faster rate.The researchers in [3] and [4] have used spiking neural networks based on pulse coding for image recognition application. The model has been developed by Gupta could recognize a set of 48 characters with 3×5 pixel size. The model of integrate and fire spiking neuron network has been utilized for recognition and the spike timing based plasticity (STDP) has been used for training [5]. Thorpe used the same neuron model to examine the recognition, but with enlarged size of images [6]. Buonomano et al. have proposed a spiking neural model which can code the intensity of the input using relative firing time for the application of position invariant character recognition [7]. Presently, researchers in the field of character recognition systems showed a strong interest to find models which mimicking the biological nature neural networks. In other words, it would be useful to inspect for such systems (image recognition) the use of neuron models which are more identical to biological nature than integrate-and-fire model. Izhikevich stated that the integrate and fire model is far from mimicking the true biological nature of spiking neuron models. He notified that integrate-and-fire model cannot satisfy most essential features of cortical spiking neurons and thereby this model has to be avoided [8]. Izhikevich has set up a comparison analysis between 11 spiking neuron models in the aspects of biological accuracy and computational load [8]. He sorted the models according to their biological accuracy and showed that the first five most biologically accurate mod...