Open Access
ARTICLE
Decision Support System Tool for Arabic Text Recognition
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.Keywords
Cite This Article
F. Baothman, S. Alssagaff and B. Ashmeel, "Decision support system tool for arabic text recognition," Intelligent Automation & Soft Computing, vol. 27, no.2, pp. 519–531, 2021.