Implementation of Feed-forward Neural Network Models for Pattern Classification Using Transformation Based Feature Extraction Methods

Authors(2) :-Sandeep Kumar, Amit Rawat

Automatic recognition of handwritten Hindi characters is a difficult and one of the most interesting research areas of pattern recognition field. A lot of work has been done in this area till date; still it is a subject of active research. Hindi characters are cursive in nature and thus characters may be written in various cursive ways. Characters also show a lot of similar features such as header line, vertical bar, curves and etc. Handwritten characters may be of varying sizes, width and orientation, which makes the problem more complicated and difficult to solve. The performance of an optical character recognition system extremely depends on the procedure used to extract quality features from characters. A number of feature extraction, classification and recognition techniques have been used successfully in this area. Proposed work is focused on some of the existing techniques like neural networks for the recognition of handwritten Hindi characters. Neural networks are good at recognizing handwritten characters as these networks are insensitive to the missing data. In this paper, we are implementing and analyzing the performance of feed-forward neural network models to perform pattern classification for handwritten Hindi characters using different transformation based feature extraction methods.

Authors and Affiliations

Sandeep Kumar
Faculty of Engineering & Technology, Agra College. Agra, Uttar Pradesh, India
Amit Rawat
Faculty of Engineering & Technology, Agra College. Agra, Uttar Pradesh, India

Feed-Forward Neural Network, Radial Basis Network, Discrete Wavelet Transform, Radon Transform, Pattern Recognition.

  1. Govindan V.K. and Shivprasad A.P., “Character Recognition: A Review”, Pattern Recognition, pg. 671-683, Vol. 23, No. 7, 1990.
  2. Plamondon R. and Srihari S.N., “On-Line and Off-Line Handwritten Character Recognition: A Comprehensive Survey,” IEEE Transactions on Pattern Analysis & Machine Intelligence, pg. 63-84, Vol. 22, No. 1, 2000.
  3. Bunke H. and Wang P.S. P., “Hand Book of Character Recognition and Document Image Analysis”, World Scientific, 1997.
  4. Starzyk J. A. and Ansari N., “Feed-forward Neural Network for Handwritten Character Recognition”, IEEE symposium on circuit and systems, 1992.
  5. Mangal M. and Singh M.P., “Handwritten English Vowels Recognition Using Hybrid Evolutionary Feed Forward Neural Networks”, Malaysian Journal of Computer Science, pp. 169-187, Vol. 19, Issue 2, 2006.
  6. Srivastava S. and Singh M.P., “ Performance Evaluation of Feed-forward Neural Network with Soft Computing Techniques for Handwritten English Alphabets”, Applied Soft Computing, pg. 1156-1182, Vol. 11, 2010.
  7. Parappa S.N. and Singh M.P., “Performance Analysis of Conjugate Descent Learning Rule of Feed Forward Neural Networks for Pattern Classification”, International Journal of Multidisciplinary and Current Research, pg. 723-725, Vol. 3, 2015.
  8. Ramteke R. J., “ Invariant Moments Based Feature Extraction for Handwritten Devanagari Vowels Recognition”, International  Journal of Computer Applications, Vol.1, No. 18, pp. 1-5, 2010.
  9. P.B. Khanale and S.D. Chitnis, “Handwritten Devanagari Character Recognition using Artificial Neural Network”. Journal of Artificial Intelligence, pg 55-62, Vol. 4, Issue 1, 2011.
  10. Agnihotri V. P., “Offline Handwritten Devanagari Script Recognition”, International Journal of Information Technology and Computer Science, pg. 37-42, Vol. 8, 2012.
  11. Gupta M. and Rana A., “Hybrid Evolutionary Techniques to Restricted Feed Forward Neural Network with Distributed Error for Recognition of Handwritten Hindi ‘MATRAS’  International Journal of Soft Computing and Engineering, pg. 161-169, Vol. 2, Issue 2, 2012. 
  12.  Singh G. and Lehri S., “ Recognition of Handwritten Hindi Characters Using Back-propagation Neural Network”, International Journal of Computer Science and Information Technologies, pg. 4892-4895, Vol. 3, Issue 4, 2012.
  13. Vaidya S. A. and Bombade B. R., “A Novel Approach of Handwritten Character Recognition Using Positional Feature Extraction”, International Journal of Computer Science and Mobile Computing, pg. 179-186, Vol. 2, Issue 6, 2013.
  14. Jaiswal G., “Handwritten Devnagari Character Recognition Model Using Neural Network”, International Journal of Engineering Development and Research, pg. 901-906, Vol. 2, Issue 1, 2014.
  15. Tanuja K., Kumari  U. V and T. M. S., “Handwritten Hindi Character Recognition System Using Edge Detection & Neural Network”, International Journal of Advanced Technology and Engineering, pg. 71-75, Vol. 2,  Issue 6, 2015. 
  16. Gonzales R.C. and Woods R.E., “Digital Image processing”, Pearson Education Pub., Third Edition, 2008.
  17. James J.F., “A Student’s Guide to Fourier Transforms – With Applications in Physics and Engineering”, Cambridge University Press, Third Edition, 2011.
  18. Goswami J.C. and Chan A.K., “Fundamentals of Wavelets – Theory, Algorithms and Applications”, Wiley & Sons Publications, Second Edition, 2011.
  19. Haykin S., “Neural Networks: A Comprehensive Foundation”, New York: Macmillan College Publishing Company Inc., 1994.
  20. Yagnanarayana B., “Artificial Intelligence”, Prentice Hall Pub., Ninth Edition, 2004.
  21. Powell M.J.D., “Radial Basis Functions for Multivariate Interpolation: A Review”, In Algorithms for the Approximation of Functions and Data, J.C. Mason and M.G. Cox, eds., Clarendon Press, pp. 143-167, 1987.

Publication Details

Published in : Volume 2 | Issue 3 | May-June 2016
Date of Publication : 2017-12-31
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 119-126
Manuscript Number : IJSRST162347
Publisher : Technoscience Academy

Print ISSN : 2395-6011, Online ISSN : 2395-602X

Cite This Article :

Sandeep Kumar, Amit Rawat , " Implementation of Feed-forward Neural Network Models for Pattern Classification Using Transformation Based Feature Extraction Methods", International Journal of Scientific Research in Science and Technology(IJSRST), Print ISSN : 2395-6011, Online ISSN : 2395-602X, Volume 2, Issue 3, pp.119-126, May-June-2016.
Journal URL :

Article Preview