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: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2014-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2014/612787 |
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| _version_ | 1850165248609222656 |
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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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