Bar chart plagiarism detection

Plagiarism can be considered one of the electronic crimes and intellectual thefts, which has become one of educational challenges of research institutions. One form to represent quantitative information is charts such as line and bar chart, which can formulate the information in info-graphic form. T...

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Main Author:	Mohammed Salih, Mohammed Mumtaz
Format:	Thesis
Language:	English
Published:	2013
Subjects:	TK Electrical engineering Electronics Nuclear engineering
Online Access:	http://eprints.utm.my/id/eprint/39164/1/MohammedMumtazMohammedSalihMFSKSM2013.pdf
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id	my-utm-ep.39164
record_format	uketd_dc
spelling	my-utm-ep.391642017-09-13T06:44:46Z Bar chart plagiarism detection 2013-01 Mohammed Salih, Mohammed Mumtaz TK Electrical engineering. Electronics Nuclear engineering Plagiarism can be considered one of the electronic crimes and intellectual thefts, which has become one of educational challenges of research institutions. One form to represent quantitative information is charts such as line and bar chart, which can formulate the information in info-graphic form. The extraction of features of bar chart is an essential process to get the data from images. Some techniques presented by researchers focused on the graphical part rather than text itself, such as Hough Transform and Learning Based method. In this study, ten features of bar chart images are utilized to detect and find the proportion of similarity between the charts. Some of these features can be directly extracted by OCR, while others demand finding the relationship between the text part and the graphic part to extract the data such as the real values for each bar in images. The new technique which introduced in this research can extract three values of each bar namely Start, End and Exact values depending on horizontal and vertical lines of the bar chart image. In addition, the Word 2-gram and Euclidean distance methods are used to detect and find the plagiarism. Experimental results show the ability of the system to detect plagiarism for ten possible patterns of bar chart plagiarisms. The performance of the system is evaluated depending on overlapping features and precision and recall. The experimental results show the ability of the system to detect not only copy and paste data of bars, but also restructuring and summarization of captions of image as well as modifications to data of bar chart images, such as swapping among bars, changing colors and changing scales of bar chart images. 2013-01 Thesis http://eprints.utm.my/id/eprint/39164/ http://eprints.utm.my/id/eprint/39164/1/MohammedMumtazMohammedSalihMFSKSM2013.pdf application/pdf en public masters Universiti Teknologi Malaysia, Faculty of Computer Science and Information Systems Faculty of Computer Science and Information Systems
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	TK Electrical engineering Electronics Nuclear engineering
spellingShingle	TK Electrical engineering Electronics Nuclear engineering Mohammed Salih, Mohammed Mumtaz Bar chart plagiarism detection
description	Plagiarism can be considered one of the electronic crimes and intellectual thefts, which has become one of educational challenges of research institutions. One form to represent quantitative information is charts such as line and bar chart, which can formulate the information in info-graphic form. The extraction of features of bar chart is an essential process to get the data from images. Some techniques presented by researchers focused on the graphical part rather than text itself, such as Hough Transform and Learning Based method. In this study, ten features of bar chart images are utilized to detect and find the proportion of similarity between the charts. Some of these features can be directly extracted by OCR, while others demand finding the relationship between the text part and the graphic part to extract the data such as the real values for each bar in images. The new technique which introduced in this research can extract three values of each bar namely Start, End and Exact values depending on horizontal and vertical lines of the bar chart image. In addition, the Word 2-gram and Euclidean distance methods are used to detect and find the plagiarism. Experimental results show the ability of the system to detect plagiarism for ten possible patterns of bar chart plagiarisms. The performance of the system is evaluated depending on overlapping features and precision and recall. The experimental results show the ability of the system to detect not only copy and paste data of bars, but also restructuring and summarization of captions of image as well as modifications to data of bar chart images, such as swapping among bars, changing colors and changing scales of bar chart images.
format	Thesis
qualification_level	Master's degree
author	Mohammed Salih, Mohammed Mumtaz
author_facet	Mohammed Salih, Mohammed Mumtaz
author_sort	Mohammed Salih, Mohammed Mumtaz
title	Bar chart plagiarism detection
title_short	Bar chart plagiarism detection
title_full	Bar chart plagiarism detection
title_fullStr	Bar chart plagiarism detection
title_full_unstemmed	Bar chart plagiarism detection
title_sort	bar chart plagiarism detection
granting_institution	Universiti Teknologi Malaysia, Faculty of Computer Science and Information Systems
granting_department	Faculty of Computer Science and Information Systems
publishDate	2013
url	http://eprints.utm.my/id/eprint/39164/1/MohammedMumtazMohammedSalihMFSKSM2013.pdf
_version_	1747816543913246720

Bar chart plagiarism detection

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