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Unsupervised Model for Detecting Plagiarism in Internet-based Handwritten Arabic Documents

Unsupervised Model for Detecting Plagiarism in Internet-based Handwritten Arabic Documents

Mahmoud Zaher, Abdulaziz Shehab, Mohamed Elhoseny, Farahat Farag Farahat
Copyright: © 2020 |Volume: 32 |Issue: 2 |Pages: 25
ISSN: 1546-2234|EISSN: 1546-5012|EISBN13: 9781522583691|DOI: 10.4018/JOEUC.2020040103
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MLA

Zaher, Mahmoud, et al. "Unsupervised Model for Detecting Plagiarism in Internet-based Handwritten Arabic Documents." JOEUC vol.32, no.2 2020: pp.42-66. http://doi.org/10.4018/JOEUC.2020040103

APA

Zaher, M., Shehab, A., Elhoseny, M., & Farahat, F. F. (2020). Unsupervised Model for Detecting Plagiarism in Internet-based Handwritten Arabic Documents. Journal of Organizational and End User Computing (JOEUC), 32(2), 42-66. http://doi.org/10.4018/JOEUC.2020040103

Chicago

Zaher, Mahmoud, et al. "Unsupervised Model for Detecting Plagiarism in Internet-based Handwritten Arabic Documents," Journal of Organizational and End User Computing (JOEUC) 32, no.2: 42-66. http://doi.org/10.4018/JOEUC.2020040103

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Abstract

Due to the rapid increase of internet-based data, there is urgent need for a robust intelligent documents security mechanism. Although there are many attempts to build a plagiarism detection system in natural language documents, the unlimited variation and different writing styles of each character in Arabic documents make building such systems challenging. Based on its position in a word, the same Arabic letter can be written three different ways, which makes the handwritten character recognition a cumbersome process. This article proposes an intelligent unsupervised model to detect plagiarism in these documents called ASTAP. First, a handwritten Arabic character recognition system is proposed using the Grey Wolf Optimization (GWO) algorithm. Then, a modified Abstract Syntax Tree (AST) is used to match the contents of the Arabic documents to detect any similarity. Compared to the state-of-the-art methods, ASTAP improves the effectiveness of the plagiarism detection in terms of the matched similarity ratio, the precision ratio, and the processing time.