Intelligent Bar Chart Plagiarism Detection in Documents
Joint Authors
al-Dhelaan, Abdullah
Salim, Naomie
Rehman, A.
Saba, T.
Alkawaz, Mohammed Hazim
Al-Dabbagh, Mohammed Mumtaz
al-Rodhaan, Mznah
Source
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-09-17
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Medicine
Information Technology and Computer Science
Abstract EN
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.
American Psychological Association (APA)
Al-Dabbagh, Mohammed Mumtaz& Salim, Naomie& Rehman, A.& Alkawaz, Mohammed Hazim& Saba, T.& al-Rodhaan, Mznah…[et al.]. 2014. Intelligent Bar Chart Plagiarism Detection in Documents. The Scientific World Journal،Vol. 2014, no. 2014, pp.1-11.
https://search.emarefa.net/detail/BIM-1050329
Modern Language Association (MLA)
Al-Dabbagh, Mohammed Mumtaz…[et al.]. Intelligent Bar Chart Plagiarism Detection in Documents. The Scientific World Journal No. 2014 (2014), pp.1-11.
https://search.emarefa.net/detail/BIM-1050329
American Medical Association (AMA)
Al-Dabbagh, Mohammed Mumtaz& Salim, Naomie& Rehman, A.& Alkawaz, Mohammed Hazim& Saba, T.& al-Rodhaan, Mznah…[et al.]. Intelligent Bar Chart Plagiarism Detection in Documents. The Scientific World Journal. 2014. Vol. 2014, no. 2014, pp.1-11.
https://search.emarefa.net/detail/BIM-1050329
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1050329
