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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...
| Autores principales: | , , , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Hindawi Publishing Corporation
2014
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4182899/ https://www.ncbi.nlm.nih.gov/pubmed/25309952 http://dx.doi.org/10.1155/2014/612787 |
| _version_ | 1782337631196020736 |
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| author | Al-Dabbagh, Mohammed Mumtaz Salim, Naomie Rehman, Amjad Alkawaz, Mohammed Hazim Saba, Tanzila Al-Rodhaan, Mznah Al-Dhelaan, Abdullah |
| author_facet | Al-Dabbagh, Mohammed Mumtaz Salim, Naomie Rehman, Amjad Alkawaz, Mohammed Hazim Saba, Tanzila Al-Rodhaan, Mznah Al-Dhelaan, Abdullah |
| author_sort | Al-Dabbagh, Mohammed Mumtaz |
| collection | PubMed |
| 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 | Online Article Text |
| id | pubmed-4182899 |
| institution | National Center for Biotechnology Information |
| language | English |
| publishDate | 2014 |
| publisher | Hindawi Publishing Corporation |
| record_format | MEDLINE/PubMed |
| spelling | pubmed-41828992014-10-12 Intelligent Bar Chart Plagiarism Detection in Documents Al-Dabbagh, Mohammed Mumtaz Salim, Naomie Rehman, Amjad Alkawaz, Mohammed Hazim Saba, Tanzila Al-Rodhaan, Mznah Al-Dhelaan, Abdullah ScientificWorldJournal Research Article 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. Hindawi Publishing Corporation 2014 2014-09-17 /pmc/articles/PMC4182899/ /pubmed/25309952 http://dx.doi.org/10.1155/2014/612787 Text en Copyright © 2014 Mohammed Mumtaz Al-Dabbagh et al. https://creativecommons.org/licenses/by/3.0/ 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. |
| spellingShingle | Research Article Al-Dabbagh, Mohammed Mumtaz Salim, Naomie Rehman, Amjad Alkawaz, Mohammed Hazim Saba, Tanzila Al-Rodhaan, Mznah Al-Dhelaan, Abdullah Intelligent Bar Chart Plagiarism Detection in Documents |
| 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 |
| topic | Research Article |
| url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4182899/ https://www.ncbi.nlm.nih.gov/pubmed/25309952 http://dx.doi.org/10.1155/2014/612787 |
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