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The Scientific World Journal
Volume 2014, Article ID 612787, 11 pages
Research Article

Intelligent Bar Chart Plagiarism Detection in Documents

1Faculty of Computing, Universiti Teknologi Malaysia, 81310 Skudai, Johor, Malaysia
2Faculty of Computer Sciences and Mathematics, University of Mosul, Mosul, Iraq
3MIS Department, CBA, Salman Bin Abdulaziz University, Alkharj, Saudi Arabia
4College of Computer and Information Sciences (CCIS), Prince Sultan University, Riyadh, Saudi Arabia
5Computer Science Department, College of Computer & Information Sciences, King Saud University, Riyadh, Saudi Arabia

Received 30 March 2014; Revised 21 June 2014; Accepted 7 July 2014; Published 17 September 2014

Academic Editor: Iftikhar Ahmad

Copyright © 2014 Mohammed Mumtaz Al-Dabbagh et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


This paper presents a novel features mining approach from documents that could not be mined via optical character recognition (OCR). By identifying the intimate relationship between the text and graphical components, the proposed technique pulls out the Start, End, and Exact values for each bar. Furthermore, the word 2-gram and Euclidean distance methods are used to accurately detect and determine plagiarism in bar charts.