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Journal : International Journal of Engineering, Science and Information Technology

Using Artificial Neural Networks and the Kohonen Method, an Image Pattern Recognition System for Khat Art Types Rasna, Rasna; Lubis, Adyanata; Suryadi, Dikky; Bani, Alexius Ulan; Nugroho, Fifto
International Journal of Engineering, Science and Information Technology Vol 5, No 2 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i2.851

Abstract

Arabic letter writing is known as khat art. Khat is classified into many categories and can be identified into three types: Khat Naskhi, Khat Qufi, and Khat Farisi, per the rules established in the art of Khat. Arabic letters, the subjects of khat art, evolved following the region where it first appeared. As a result, the Qufi style, for instance, marked the start of the evolution of Khat in the tenth century. Previously somewhat rigid, Khat became more fluid and beautiful, although it remained angular. Subsequently, the art of Sulus, Naskhi, Raiham, Riqa, and Tauqi evolved and exhibited the form of Khat, cursive (italic)—artificial neural network-based khat art type recognition by selecting the Kohonen