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Reseach Article

An Unsupervised Intelligent System to Detect Fabrication in Photocopy Document using Geometric Moments and Gray Level Co-Occurrence Matrix

by Suman V Patgar, Vasudev T
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 74 - Number 12
Year of Publication: 2013
Authors: Suman V Patgar, Vasudev T
10.5120/12939-9995

Suman V Patgar, Vasudev T . An Unsupervised Intelligent System to Detect Fabrication in Photocopy Document using Geometric Moments and Gray Level Co-Occurrence Matrix. International Journal of Computer Applications. 74, 12 ( July 2013), 29-35. DOI=10.5120/12939-9995

@article{ 10.5120/12939-9995,
author = { Suman V Patgar, Vasudev T },
title = { An Unsupervised Intelligent System to Detect Fabrication in Photocopy Document using Geometric Moments and Gray Level Co-Occurrence Matrix },
journal = { International Journal of Computer Applications },
issue_date = { July 2013 },
volume = { 74 },
number = { 12 },
month = { July },
year = { 2013 },
issn = { 0975-8887 },
pages = { 29-35 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume74/number12/12939-9995/ },
doi = { 10.5120/12939-9995 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:42:06.816593+05:30
%A Suman V Patgar
%A Vasudev T
%T An Unsupervised Intelligent System to Detect Fabrication in Photocopy Document using Geometric Moments and Gray Level Co-Occurrence Matrix
%J International Journal of Computer Applications
%@ 0975-8887
%V 74
%N 12
%P 29-35
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Photocopy documents are very common in our normal life. People are permitted to carry and produce photocopied documents frequently, to avoid damages or losing the original documents. But this provision is misused for temporary benefits by fabricating fake photocopied documents. When a photocopied document is produced, it may be required to check for its originality. An attempt is made in this direction to detect such fabricated photocopied documents. This paper proposes an unsupervised two level classification system to detect fabricated photocopied document using Geometric moments and Gray Level Co-Occurrence Matrix features. The work in this paper mainly focuses on detecting fabrication of photocopied document in which some contents are manipulated by smearing whitener over the original content and writing new contents above it. A detailed experimental study has been performed using a collected sample set of considerable size and a decision model is developed for classification. Testing is performed with a different set of collected testing samples resulted in an average detection rate of 94. 59%.

References
  1. Vasudev T, 2007, Automatic Data Extraction from Pre-Printed Input Data Forms: Some New Approaches, PhD thesis supervised by Dr. G. Hemanthakumar, University of Mysore, India.
  2. Rich Kevin Knight, Artificial Intelligence, 2nd Edition, McGraw-Hill Higher Education.
  3. Madasu Hanmandlu, Mohd. Hafizuddin, Mohd. Yusof, Vamsi Krishna Madasu, 2005,Off-line signature verification and forgery detection using fuzzy modeling, Pattern Recognition Vol. 38, pp 341-356.
  4. Cha, S. -H. , & Tapert, C. C. ,2002, Automatic Detection of Handwriting forgery, Proc. 8thInt. Workshop Frontiers Handwriting Recognition(IWFHR-8), Niagara, Canada, pp 264-267.
  5. Christoph H Lampert, Lin Mei, Thomas M Breuel, 2006, Printing Technique Classification for Document Counterfeit Detection Computational Intelligence and Security, International Conference, Vol. 1, pp 639-644.
  6. Utpal Garian, Biswajith Halder, 2008, On Automatic Authenticity Verification of Printed Security Documents, IEEE Computer Society Sixth Indian Conference on Computer vision, Graphics & Image Processing, pp 706-713.
  7. Angelo Frosini, Marco Gori, Paolo Priami, Nov 1996, A Neural Network-Based Model For paper Currency Recognition and Verification, IEEE Transactions on Neural Networks, Vol. 7, No. 6,
  8. Kuo Chin Fan, 2001, Marginal Noise Removal of Document Image, ICDAR 01, pp 317-321.
  9. Rafael C Gonzales & Richard E Woods, 2002, Digital Image Processing, 2nd Edition, Pearson Education Publication.
  10. Jain A K, 1998, Fundamentals of Digital Image Processing, Prentice Hall, Englewood Cliffs, NJ.
  11. R Haralick K Shanmugam and I Dinstein, 1973 "Textural Features for Image classification", IEEE Trans on system Man and Cybernetics SMC-3(6): 610-621,.
  12. Angadi S A, 2007,Postal Automation, PhD thesis submitted under the supervision of Dr. Nagabhushan P, University of Mysore, India
  13. L. H. Siew, R. H. Hodgson, and E. J. Wood, 1988, Texture Measures for Carpet Wear Asessment, IEEE Trans. on Pattern Analysis and Machine Intell. , Vol. PAMI-10, pp. 92-105.
  14. D. C He, L Wang and J Juibert, Texture Feature extraction, Pattern Recognition Letter, Vol 6 pp 269-273.
  15. Rishi Jobanputra, David A Clausi, 2006, Preserving boundaries for image texture segmentation using greylevel co-occurring probabilities Pattern Recognition 39 234-245.
Index Terms

Computer Science
Information Sciences

Keywords

Fabricated photocopy document Geometric Moments Gray Level Co-occurrence Matrix GLCM features text contour