@Article{iasc.2021.014828,
AUTHOR = {Fatmah Baothman, Sarah Alssagaff, Bayan Ashmeel},
TITLE = {Decision Support System Tool for Arabic Text Recognition},
JOURNAL = {Intelligent Automation \& Soft Computing},
VOLUME = {27},
YEAR = {2021},
NUMBER = {2},
PAGES = {519--531},
URL = {http://www.techscience.com/iasc/v27n2/41251},
ISSN = {2326-005X},
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.},
DOI = {10.32604/iasc.2021.014828}
}