Intelligent Bar Chart Plagiarism Detection in Documents

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. Fur...

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Main Authors: Mohammed Mumtaz Al-Dabbagh, Naomie Salim, Amjad Rehman, Mohammed Hazim Alkawaz, Tanzila Saba, Mznah Al-Rodhaan, Abdullah Al-Dhelaan
Format: Article
Language:English
Published: Wiley 2014-01-01
Series:The Scientific World Journal
Online Access:http://dx.doi.org/10.1155/2014/612787
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author Mohammed Mumtaz Al-Dabbagh
Naomie Salim
Amjad Rehman
Mohammed Hazim Alkawaz
Tanzila Saba
Mznah Al-Rodhaan
Abdullah Al-Dhelaan
author_facet Mohammed Mumtaz Al-Dabbagh
Naomie Salim
Amjad Rehman
Mohammed Hazim Alkawaz
Tanzila Saba
Mznah Al-Rodhaan
Abdullah Al-Dhelaan
author_sort Mohammed Mumtaz Al-Dabbagh
collection DOAJ
description 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.
format Article
id doaj-art-ec399454af9b4e57a8072be01a038dc4
institution OA Journals
issn 2356-6140
1537-744X
language English
publishDate 2014-01-01
publisher Wiley
record_format Article
series The Scientific World Journal
spelling doaj-art-ec399454af9b4e57a8072be01a038dc42025-08-20T02:21:47ZengWileyThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/612787612787Intelligent Bar Chart Plagiarism Detection in DocumentsMohammed Mumtaz Al-Dabbagh0Naomie Salim1Amjad Rehman2Mohammed Hazim Alkawaz3Tanzila Saba4Mznah Al-Rodhaan5Abdullah Al-Dhelaan6Faculty of Computing, Universiti Teknologi Malaysia, 81310 Skudai, Johor, MalaysiaFaculty of Computing, Universiti Teknologi Malaysia, 81310 Skudai, Johor, MalaysiaMIS Department, CBA, Salman Bin Abdulaziz University, Alkharj, Saudi ArabiaFaculty of Computing, Universiti Teknologi Malaysia, 81310 Skudai, Johor, MalaysiaCollege of Computer and Information Sciences (CCIS), Prince Sultan University, Riyadh, Saudi ArabiaComputer Science Department, College of Computer & Information Sciences, King Saud University, Riyadh, Saudi ArabiaComputer Science Department, College of Computer & Information Sciences, King Saud University, Riyadh, Saudi ArabiaThis 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.http://dx.doi.org/10.1155/2014/612787
spellingShingle Mohammed Mumtaz Al-Dabbagh
Naomie Salim
Amjad Rehman
Mohammed Hazim Alkawaz
Tanzila Saba
Mznah Al-Rodhaan
Abdullah Al-Dhelaan
Intelligent Bar Chart Plagiarism Detection in Documents
The Scientific World Journal
title Intelligent Bar Chart Plagiarism Detection in Documents
title_full Intelligent Bar Chart Plagiarism Detection in Documents
title_fullStr Intelligent Bar Chart Plagiarism Detection in Documents
title_full_unstemmed Intelligent Bar Chart Plagiarism Detection in Documents
title_short Intelligent Bar Chart Plagiarism Detection in Documents
title_sort intelligent bar chart plagiarism detection in documents
url http://dx.doi.org/10.1155/2014/612787
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AT mohammedhazimalkawaz intelligentbarchartplagiarismdetectionindocuments
AT tanzilasaba intelligentbarchartplagiarismdetectionindocuments
AT mznahalrodhaan intelligentbarchartplagiarismdetectionindocuments
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