CFP last date
20 January 2025
Call for Paper
February Edition
IJCA solicits high quality original research papers for the upcoming February edition of the journal. The last date of research paper submission is 20 January 2025

Submit your paper
Know more
The week's pick
Responsive image
SFLA-Based Line Balancing and Task Optimization in Master-Slave Controlled Hanger Transportation Systems for Garment Production

Duc Hoang Nguyen
Random Articles
Reseach Article

Handwriting Recognition System- A Review

by Pooja Yadav, Nidhika Yadav
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 114 - Number 19
Year of Publication: 2015
Authors: Pooja Yadav, Nidhika Yadav
10.5120/20090-2131

Pooja Yadav, Nidhika Yadav . Handwriting Recognition System- A Review. International Journal of Computer Applications. 114, 19 ( March 2015), 36-40. DOI=10.5120/20090-2131

@article{ 10.5120/20090-2131,
author = { Pooja Yadav, Nidhika Yadav },
title = { Handwriting Recognition System- A Review },
journal = { International Journal of Computer Applications },
issue_date = { March 2015 },
volume = { 114 },
number = { 19 },
month = { March },
year = { 2015 },
issn = { 0975-8887 },
pages = { 36-40 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume114/number19/20090-2131/ },
doi = { 10.5120/20090-2131 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:53:21.143249+05:30
%A Pooja Yadav
%A Nidhika Yadav
%T Handwriting Recognition System- A Review
%J International Journal of Computer Applications
%@ 0975-8887
%V 114
%N 19
%P 36-40
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Handwriting recognition has been an active and challenging area of research. Handwriting recognition system plays a very important role in today's world. Handwriting recognition is very popular and computationally expensive work. At present time it is very difficult to find correct meaning of handwritten documents. There are many areas where we need to recognize the words, alphabets and digit. There are many application postal addresses, bank cheque where we need to recognize handwriting. This review paper will focus on different technique which is used on handwriting recognition. There are basically two different types of handwriting recognition system online and offline handwriting recognition. There are many approaches are present for offline handwriting recognition system. This review paper will represent the limitations and superiorities of different technique which is used for handwriting recognition system. So handwriting recognition has been studied from many decades. Handwriting recognition system can be used to solve many complex problems and can make human's work easy. So this paper is an overview of different approaches of handwriting recognition system with their limitations and accuracy rate.

References
  1. Kai Ding, Zhibin Liu, Lianwen Jin, Xinghua Zhu, A Comparative study of GABOR feature and gradient feature for handwritten 17hinese character recognition, International Conference on Wavelet Analysis and Pattern Recognition, pp. 1182-1186, Beijing, China, 2-4 Nov. 2007.
  2. Z. Xinbo and W. Lili, "Handwritten Digit Recognition Based on Improced Leaning Rate BP Algorithm". The 2nd International Conference on Information Engineering and Computer Science (ICIECS), pp. 1-4. 2010.
  3. J. Ahmed and E. M. Alkhalifa. "Efficient Single Layer Handwritten Digit Recognition through an Optimizing Algorithm", Proceedings of the 9thInternational Conference on Neural Information Processing (ICONIP). Vol. 5. pp. 2464-2468. 2002
  4. S. Russell and P. Norvig. "Artificial Intelligence: A Modern Approach" 2nd Edition. Prentice Hall, 2002
  5. UCI Machine Learning Repository – Optical Recognition of Handwritten Digits Dataset. http://archive. ics. uci. edu/ml/datasets/Optical+Recognition+of+Handwrit ten+Digits
  6. John Seiffertt. "Back propagation and Ordered in the Time Scales Calculus", IEEE Transactions on Neural Networks, Vol. 21, No. 8, pp. 1262-1269. 2010.
  7. F. M Kimura, M. Shridhar, "Handwrite Multiple algorithms", Pattern Recognize 1991.
  8. M. Hanmandlu, K. R. M. Mohan an Handwritten character recognition," Conference on Document Analysis and
  9. E. M. Kwd. Associative neuron-like structures. //Kiev,Naukova Dumka, 1992, 144 pp. ISBN 5-12-002760-1 (in Russian).
  10. E. M. Kussd, T. N. Baydyk, D. A. Rachkovskij. Application of neural network classifiers for OCR of printed text. The SecondIntematiod Symposium on Neuroinfiormatics andNeurocomputers. Rostov-on-Don, Russia, September, 20-23, 1995.
  11. S. Mori, C. Y. Suen and K. Kamamoto, "Historical review of OCRresearch and development," Proc. of IEEE, vol. 80, pp. 1029-1058, July1992.
  12. N. Arica and F. Yarman-Vural, "An Overview of Character Recognition Focused on Off-line Handwriting", IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, Vol. 31 (2), pp. 216 - 233. 2001.
  13. V. K. Govindan and A. P. Shivaprasad, "Character Recognition – A review," Pattern Recognition, Vol. 23, no. 7, pp. 671- 683, 1990.
Index Terms

Computer Science
Information Sciences

Keywords

Handwriting recognition neural network MatLab

Powered by PhDFocusTM