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All Journal JOMLAI: Journal of Machine Learning and Artificial Intelligence
Ziqra Addilah
Universitas Islam Negeri Sumatera Utara, Medan, Indonesia
Computer Science & IT Engineering

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Calligraphy Text Types Recognition Using Learning Vector Quantization Mhd Furqan; Abdul Halim Hasugian; Ziqra Addilah
JOMLAI: Journal of Machine Learning and Artificial Intelligence Vol. 1 No. 4 (2022): December
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (527.993 KB) | DOI: 10.55123/jomlai.v1i4.1653

Abstract

Calligraphy is the art of beautiful writing. The term calligraphy comes from simplified English (calligraphy) taken from the Latin word "kalios" which means beautiful, and "graph" which means writing or script. The art of writing Arabic letters is called the science of khat, known as the science of Arabic calligraphy or Islamic calligraphy. There are many types and varieties of Islamic calligraphy. Each has a different form and function. There are seven types of calligraphy that are popular and known by lovers of calligraphy art in Indonesia, such as, Khat Naskhi, Tsuluts, Farisi, Riq'ah, Diwani, Diwani Jali, and Kufi. The method commonly used to identify what type of calligraphy is made is by looking directly at the shape and characteristics of the calligraphy itself (calligraphy experts). Here the author tries to create a computerized calligraphy type recognition system using the Learning Vector Quantization method. Where this method is a method that works with each unit of output representing a class. So with this system, we can recognize the type of calligraphy text computerized. The accuracy value obtained in the results of calligraphy image recognition is 75%.
Co-Authors Abdul Halim Hasugian Mhd Furqan