CFP last date
22 July 2024
Reseach Article

A Survey on Feature Extraction Methods for Handwritten Digits Recognition

by Ishani Patel, Virag Jagtap, Ompriya Kale
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 107 - Number 12
Year of Publication: 2014
Authors: Ishani Patel, Virag Jagtap, Ompriya Kale
10.5120/18801-0317

Ishani Patel, Virag Jagtap, Ompriya Kale . A Survey on Feature Extraction Methods for Handwritten Digits Recognition. International Journal of Computer Applications. 107, 12 ( December 2014), 11-17. DOI=10.5120/18801-0317

@article{ 10.5120/18801-0317,
author = { Ishani Patel, Virag Jagtap, Ompriya Kale },
title = { A Survey on Feature Extraction Methods for Handwritten Digits Recognition },
journal = { International Journal of Computer Applications },
issue_date = { December 2014 },
volume = { 107 },
number = { 12 },
month = { December },
year = { 2014 },
issn = { 0975-8887 },
pages = { 11-17 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume107/number12/18801-0317/ },
doi = { 10.5120/18801-0317 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:40:51.982844+05:30
%A Ishani Patel
%A Virag Jagtap
%A Ompriya Kale
%T A Survey on Feature Extraction Methods for Handwritten Digits Recognition
%J International Journal of Computer Applications
%@ 0975-8887
%V 107
%N 12
%P 11-17
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Hand written digit recognition is highly nonlinear problem. Recognition of handwritten numerals plays an active role in day to day life now days. Office automation, e-governors and many other areas, reading printed or handwritten documents and convert them to digital media is very crucial and time consuming task. So the system should be designed in such a way that it should be capable of reading handwritten numerals and provide appropriate response as humans do. However, handwritten digits are varying from person to person because each one has their own style of writing, means the same digit or character/word written by different writer will be different even in different languages. This paper presents survey on handwritten digit recognition systems with recent techniques, with three well known classifiers namely MLP, SVM and k-NN used for classification. This paper presents comparative analysis that describes recent methods and helps to find future scope.

References
  1. J. Pradeep, E. Srinivasan And S. Himavathi,"Diagonal Based Feature Extraction For Handwritten Alphabets Recognition System Using Neural Network", IEEE Conference On Electronics Computer Technology, Feb 2011, Vol 3, No 1, Pages. 27-38.
  2. Water Reservoir Based Approach For Touching Numeral Segmentation ,U. Pal, A. Belaïd,And C. Choisy , Springer 2003.
  3. U Ravi Babu, Dr. Y Venkateswarlu, Aneel Kumar Chintha,"Handwritten Digit Recognition Using K-Nearest Neighbor Classifier" 2014 World Congress On Computing And Communication Technologies,Vol 4,Pages. 60-65.
  4. Olarik Surinta, Lambert Schomaker and Macro Wiering "A Comparison of Feature And Pixel- Based Methods For Recognizing Handwritten Bangla Digits " ,2013 12th International Conference On Document Analysis And Recognition, August 2013, Pages-165-169
  5. Kartar Singh Siddharth, Renu Dhir, Rajneesh Rani, "Comparative Recognition of Hand Written Gurumukhi Numerals Using Different Feature Sets And Classifiers" , 2012 International Journal of Computer Applications, Pages. 20-24.
  6. S. V Rajasheararadhya, Dr P. Vanaja Ranjan "Efficient Zone Based Feature Extraction Algorithm for Handwritten Numeral Recognition of Four Popular South Indian Scripts" Journal of Theoretical and Information Technology April, 2008, Pages. 1171-1180.
  7. Mamta Garg Et. Al. "A Novel Approach to Recognize the Off-line Handwritten Numerals using MLP and SVM Classifier". IJCSET 2013, Vol. 4 No. 07 July 2013, Pages. 953-958.
  8. S N Sivanandam, S Sumathi, S N Deepa "Introduction to Neural Networks Using MATLAB6. 0" Mc Graw Hill Education 2013
  9. Simon, Hykin "Neural Networks and Learning Machines", PHP 2013
  10. Han, Kamber, Pei "Data mining Concepts & Techniques", Morgan Kaufman MIT press.
  11. Olarik Surinta, Lambert schomaker and Macro Wiering , "Handwritten Character Classification Using the HotSpot Feature Extraction Techniques " ICPRAM 2012- International Conference on Pattern Recognition Application and Methods, Pages. 261-264.
  12. Imran Siddiqi, Nicole Vincent, "A set of Chain code based features for Writer Recognition" IEEE 2009 10th International Conference on document analysis and Recognition, Pages. 981-985.
  13. R. C Gonzal, R. E. Woods, "Digital Image Processing", Pearson education, 2002
  14. Manish Shah et al. "A literature review on handwritten character recognition" ISRJ, ISSN: 22307850 vol-3 March 2013
  15. Wen Yu, N. Sanchez, "Advances in Computational Intelligence"
  16. Springer Science & Business Media, 18-Aug-2009. O. L. Mangasarian, David R. Musicant, "Lagrangian Support Vector Machines", Journal of Machine Learning research 1(2001) pages. 161-177.
Index Terms

Computer Science
Information Sciences

Keywords

Feature Extraction Back Propagation (BP) k-Nearest Neighbor (k-NN) Support Vector Machine (SVM).