Abstract
Neural networks, or the artificial neural networks to be more precise, represents a technology that is rooted in many disciplines: neuroscience, mathematics, statistics, physics, computer science and engineering. Neural network finds applications in such fields as modeling, time series analysis, pattern recognition signal processing and control by virtue of an important property: the ability to learn from input data with or without a teacher .In a biological system, learning involves adjustments to the synaptic connections between neurons same for artificial neural networks (ANNs) works too that has made it applicable to valid applications. Neural Network architecture has the ability to learn for the things and then later on classify the things. Neural Network for Character Recognition is based over Multilayered Architecture having Back-propagation algorithm. First Network is been trained for the alphanumeric handwritten characters and then testing the network with the trained or untrained handwritten characters. We achieved a greater computation enhancement by using modified back- propagation algorithm having an added momentum term, which lowers the training time and speeds the system. The time is more reduced with its parallel implementation using CUDA.
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© 2013 Springer-Verlag Berlin Heidelberg
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Pal, A., Srivastava, K.K., Kumar, A. (2013). Parallel Character Reconstruction Expending Compute Unified Device Architecture. In: Meghanathan, N., Nagamalai, D., Chaki, N. (eds) Advances in Computing and Information Technology. Advances in Intelligent Systems and Computing, vol 177. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31552-7_64
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DOI: https://doi.org/10.1007/978-3-642-31552-7_64
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31551-0
Online ISBN: 978-3-642-31552-7
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