NDLI: Review of Handwritten Pattern Recognition of Digits and Special Characters Using Feed Forward Neural Network and Izhikevich Neural Model

Content Provider	IEEE Xplore Digital Library
Author	Chaturvedi, S. Titre, R.N. Sondhiya, N.
Copyright Year	2014
Description	Author affiliation: PIET, Nagpur, India (Titre, R.N.; Sondhiya, N.) \|\| Electron. & Commun., PIET, Nagpur, India (Chaturvedi, S.)
Abstract	Neural Networks are found as an effective tool for pattern recognition. In this paper a Feed Forward Neural Network and an Izhikevich neuron model is applied for pattern recognition of Digits and Special characters. Given a set of input patterns of digits and Special characters each input pattern is transformed into an input signal. Then the Feed Forward Neural Network and Izhikevich neuron model is stimulated and firing rates are computed. After adjusting the synaptic weights and the threshold values of the neural model, input patterns will generate almost the same firing rate and will recognize the patterns. At last, a comparison between a feed-forward neural network which is Artificial Neural Network model and the Izhikevich neural model which is Spiking Neural Network model is implemented in MATLAB for the handwritten Pattern recognition.
Sponsorship	IEEE Bombay Sect.
Starting Page	425
Ending Page	428
File Size	213211
Page Count	4
File Format	PDF
ISBN	9781479921027
DOI	10.1109/ICESC.2014.83
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-01-09
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	ANN (Artificial Neural Network) Computational modeling Biological system modeling Neurons SNN (Spiking Neural Network) Artificial neural networks Izhikevich Neuron Model Feature extraction FFNN (Feed Forward Neural Network) Pattern recognition Biological neural networks
Content Type	Text
Resource Type	Article

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