We apologize for a recent technical issue with our email system, which temporarily affected account activations. Accounts have now been activated. Authors may proceed with paper submissions. PhDFocusTM
CFP last date
20 November 2024
Reseach Article

Digit Recognition System by using Back Propagation Algorithm

by V. Kapoor, Priyanka Gupta
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 83 - Number 8
Year of Publication: 2013
Authors: V. Kapoor, Priyanka Gupta
10.5120/14471-2762

V. Kapoor, Priyanka Gupta . Digit Recognition System by using Back Propagation Algorithm. International Journal of Computer Applications. 83, 8 ( December 2013), 33-36. DOI=10.5120/14471-2762

@article{ 10.5120/14471-2762,
author = { V. Kapoor, Priyanka Gupta },
title = { Digit Recognition System by using Back Propagation Algorithm },
journal = { International Journal of Computer Applications },
issue_date = { December 2013 },
volume = { 83 },
number = { 8 },
month = { December },
year = { 2013 },
issn = { 0975-8887 },
pages = { 33-36 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume83/number8/14471-2762/ },
doi = { 10.5120/14471-2762 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:58:50.795602+05:30
%A V. Kapoor
%A Priyanka Gupta
%T Digit Recognition System by using Back Propagation Algorithm
%J International Journal of Computer Applications
%@ 0975-8887
%V 83
%N 8
%P 33-36
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

An artificial neural network is configured for a specific application, such as pattern recognition or data classification, through a learning process. Just like biological systems involves adjustments to the synaptic connections that exist between the neurons, artificial neural network also works on the same principle. The work described in this research does not have the intention to compete with existing systems, but merely served to illustrate to the general public how an artificial neural network can be used to recognize handwritten digits.

References
  1. K. Ding, Z. Liu, L. Jin, X. Zhu, "A Comparative study of GABOR feature and gradient feature for handwritten chinese character recognition", International Conference on Wavelet Analysis and Pattern Recognition, pp. 1182-1186, Beijing, China, 2-4 Nov. 2007.
  2. P. Charles, V. Harish, M. Swathi, C. Deepthi, "A Review on the Various Techniques used for Optical Character Recognition", International Journal of Engineering Research and Applications, Vol. 2, Issue 1, pp. 659-662, Jan-Feb 2012.
  3. L. Cheng-Lin, Nakashima, Kazuki, H. Sako, H. Fujisawa, "Handwritten digit recognition: investigation of normalization and feature extraction techniques, Pattern Recognition", Vol. 37, No. 2, pp. 265-279, 2004.
  4. S. Patil, H. Bhagat, " Character Recognition System Using Back Prorogation Network ", International Journal of Advanced Research in Computer Science and Software Engineering, Vol. 3, Issue 8, pp. 1229-1233, 2013.
  5. J. Pradeepa, E. Srinivasana, S. Himavathib, "Neural Network Based Recognition System Integrating Feature Extraction and Classification for English Handwritten", International journal of Engineering,Vol. 25, No. 2, pp. 99-106, May 2012.
  6. T. Dash, "Time efficient approach to offline hand written character recognition using associative memory net", International Journal of Computing and Business Research, Volume 3 Issue 3 September 2012.
  7. R. Tokas, A. Bhadu, "A comparative analysis of feature extraction techniques for handwritten character recognition", International Journal of Advanced Technology & Engineering Research, Volume 2, Issue 4, pp. 215-219, July 2012.
  8. A. Sampath, C. Tripti, V. Govindaru, "Freeman code based online handwritten character recognition for Malayalam using backpropagation neural networks", International journal on Advanced computing, Vol. 3, No. 4, pp. 51 - 58, July 2012.
  9. R. Tiwari, A. Vishwanath, D. Wadhone, "Handwritten Digit Recognition Using Back propagation Neural Network& K-Nearest Neighbour Classifier", International Journal of Electrical, Electronics and Data Communication, ISSN: 2320-2084 Volume- 1, Issue- 5.
Index Terms

Computer Science
Information Sciences

Keywords

BPNN Patten Recognition Neural Network epoch etc.