OcrV1, PubMed, Checkpoint, bibRecord, 000014

Intelligent bar chart plagiarism detection in documents.

Identifieur interne : 000014 ( PubMed/Checkpoint ); précédent : 000013; suivant : 000015

Intelligent bar chart plagiarism detection in documents.

Auteurs : Mohammed Mumtaz Al-Dabbagh [Iraq] ; Naomie Salim [Malaisie] ; Amjad Rehman [Arabie saoudite] ; Mohammed Hazim Alkawaz [Iraq] ; Tanzila Saba [Arabie saoudite] ; Mznah Al-Rodhaan [Arabie saoudite] ; Abdullah Al-Dhelaan [Arabie saoudite]

Source :

TheScientificWorldJournal [ 1537-744X ] ; 2014.

RBID : pubmed:25309952

English descriptors

KwdEn :
- Algorithms, Artificial Intelligence, Computer Graphics, Data Mining (methods), Humans, Pattern Recognition, Automated (methods), Plagiarism, Semantics.
MESH :
- methods : Data Mining, Pattern Recognition, Automated.
- Algorithms, Artificial Intelligence, Computer Graphics, Humans, Plagiarism, Semantics.

Abstract

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.

DOI: 10.1155/2014/612787
PubMed: 25309952

Affiliations:

Arabie saoudite, Iraq, Malaisie

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pubmed:25309952

Le document en format XML

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Intelligent bar chart plagiarism detection in documents.

Intelligent bar chart plagiarism detection in documents.

Source :

English descriptors

Abstract

Links toward previous steps (curation, corpus...)

Links to Exploration step

Le document en format XML

Pour manipuler ce document sous Unix (Dilib)

Pour mettre un lien sur cette page dans le réseau Wicri

Pour générer des pages wiki