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Applied Computational Intelligence and Soft Computing
Volume 2015, Article ID 193868, 12 pages
Research Article

On the Performance Improvement of Devanagari Handwritten Character Recognition

1IET, DAVV, Khandwa Road, Indore 452017, India
2IIT, Khandwa Road, Indore 452017, India

Received 3 August 2014; Revised 29 December 2014; Accepted 16 January 2015

Academic Editor: Ying-Tung Hsiao

Copyright © 2015 Pratibha Singh et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The paper is about the application of mini minibatch stochastic gradient descent (SGD) based learning applied to Multilayer Perceptron in the domain of isolated Devanagari handwritten character/numeral recognition. This technique reduces the variance in the estimate of the gradient and often makes better use of the hierarchical memory organization in modern computers. -weight decay is added on minibatch SGD to avoid overfitting. The experiments are conducted firstly on the direct pixel intensity values as features. After that, the experiments are performed on the proposed flexible zone based gradient feature extraction algorithm. The results are promising on most of the standard dataset of Devanagari characters/numerals.