Open Access

ARTICLE

Decision Support System Tool for Arabic Text Recognition

Fatmah Baothman^*, Sarah Alssagaff, Bayan Ashmeel

Information System Department, King Abdul-Aziz University, Jeddah, 21551, Saudi Arabia

* Corresponding Author: Fatmah Baothman. Email:

(This article belongs to this Special Issue: Computational Intelligence for Internet of Medical Things and Big Data Analytics)

Intelligent Automation & Soft Computing 2021, 27(2), 519-531. https://doi.org/10.32604/iasc.2021.014828

Received 20 October 2020; Accepted 14 December 2020; Issue published 18 January 2021

Abstract

The National Center for Education Statistics study reported that 80% of students change their major or institution at least once before getting a degree, which requires a course equivalency process. This error-prone process varies among disciplines, institutions, regions, and countries and requires effort and time. Therefore, this study aims to overcome these issues by developing a decision support tool called TiMELY for automatic Arabic text recognition using artificial intelligence techniques. The developed tool can process a complete document analysis for several course descriptions in multiple file formats, such as Word, Text, Pages, JPEG, GIF, and JPG. We applied a comparative approach in selecting the highest score using three Arabic text extraction algorithms: term frequency-inverse document frequency measure algorithm, Cortical.io tool with Retina Database, and keyword extraction using word co-occurrence algorithm. The data repository consisted of 1000 datasets built from five different faculties at King Abdul-Aziz University and King Faisal University. It was followed by a discussion of the evaluation techniques using precision and recall measurements, which indicated that the keyword extraction using word co-occurrence algorithm scored 90% for the English language and 80% for the Arabic language in terms of the F1 measure that focuses on the linguistic relation between words.

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