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All Journal Khazanah Journal of Religion and Technology
Khalifaturohman, Muhamad Khansa
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Religion Humanities Computer Science & IT Engineering Social Sciences

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Systematic Literature Review: Deep Learning Models in Arabic Script Classification Khalifaturohman, Muhamad Khansa
Khazanah Journal of Religion and Technology Vol. 3 No. 1 (2025): June
Publisher : UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/kjrt.v3i1.1619

Abstract

Arabic calligraphy is an essential element of Islamic art, and is now widely developed in digital form. With the advancement of artificial intelligence technology, particularly Convolutional Neural Networks (CNNs), several studies have been conducted to classify the styles, characters, and authenticate Arabic calligraphy. This study aims to conduct a systematic literature review on the application of CNNs in the recognition and classification of Arabic calligraphy. The identification process was carried out by searching several scientific databases and screening 152 articles, but only five studies met the criteria for relevance and eligibility. The results of the study indicate that the application of CNNs in this domain is still limited and dominated by a focus on style or letter classification, while topics such as authenticity of original works and AI-generated calligraphy detection are still very rarely researched. The limited number of available studies indicates that this topic is an open area for further exploration in the academic realm and the development of digital Islamic art preservation technology.
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