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Intelligent bar chart plagiarism detection in documents.

Mohammed Mumtaz Al-Dabbagh1, Naomie Salim2, Amjad Rehman3

  • 1Faculty of Computing, Universiti Teknologi Malaysia, 81310 Skudai, Johor, Malaysia ; Faculty of Computer Sciences and Mathematics, University of Mosul, Mosul, Iraq.

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This summary is machine-generated.

This study introduces a new method to extract data from documents unsuitable for optical character recognition (OCR). The technique accurately identifies bar chart data and detects plagiarism using text and graphical analysis.

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Area of Science:

  • Data mining
  • Document analysis
  • Computer vision

Background:

  • Traditional optical character recognition (OCR) struggles with extracting data from complex documents containing graphical elements.
  • Many documents with charts and text lack machine-readable data, limiting information retrieval.
  • Plagiarism detection in visual data, such as bar charts, remains a challenge.

Purpose of the Study:

  • To present a novel features mining approach for documents not amenable to OCR.
  • To extract precise data values (Start, End, Exact) from bar charts by analyzing text-graphical relationships.
  • To develop a robust method for detecting plagiarism in bar charts using textual and graphical features.

Main Methods:

  • Analyzing the interplay between textual and graphical components within documents.
  • Implementing a technique to extract Start, End, and Exact values from bar chart elements.
  • Utilizing word 2-gram and Euclidean distance algorithms for plagiarism detection in bar charts.

Main Results:

  • Successfully extracted key data points from bar charts in documents previously inaccessible to OCR.
  • Demonstrated the capability to identify and quantify plagiarism within bar chart visualizations.
  • Achieved accurate data extraction and plagiarism detection through the proposed integrated approach.

Conclusions:

  • The novel features mining approach effectively overcomes OCR limitations for data extraction from graphical documents.
  • The method provides a reliable solution for identifying and preventing plagiarism in visual data representations.
  • This technique enhances information retrieval and integrity in document analysis.