Fast learning neural network based on texture for Arabic calligraphy identification

Arabic calligraphy is considered a sort of Arabic writing art where letters in Arabic can be written in various curvy or segments styles. The efforts of automating the identification of Arabic calligraphy by using artificial intelligence were less comparing with other languages. Hence, this article...

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Main Author: Kawther Hussein, Ahmed (Author)
Format: EJournal Article
Published: Institute of Advanced Engineering and Science, 2021-03-01.
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042 |a dc 
100 1 0 |a Kawther Hussein, Ahmed  |e author 
100 1 0 |e contributor 
245 0 0 |a Fast learning neural network based on texture for Arabic calligraphy identification 
260 |b Institute of Advanced Engineering and Science,   |c 2021-03-01. 
500 |a https://ijeecs.iaescore.com/index.php/IJEECS/article/view/23393 
520 |a Arabic calligraphy is considered a sort of Arabic writing art where letters in Arabic can be written in various curvy or segments styles. The efforts of automating the identification of Arabic calligraphy by using artificial intelligence were less comparing with other languages. Hence, this article proposes using four types of features and a single hidden layer neural network for training on Arabic calligraphy and predicting the type of calligraphy that is used. For neural networks, we compared the case of non-connected input and output layers in extreme learning machine ELM and the case of connected input-output layers in FLN. The prediction accuracy of fast learning machine FLN was superior comparing ELM that showed a variation in the obtained accuracy.  
540 |a Copyright (c) 2021 Institute of Advanced Engineering and Science 
540 |a http://creativecommons.org/licenses/by-nc/4.0 
546 |a eng 
690
690 |a Arabic calligraphy; Binarized statistical image; Extreme learning machine; Fast learning machine; Local phase quantization 
655 7 |a info:eu-repo/semantics/article  |2 local 
655 7 |a info:eu-repo/semantics/publishedVersion  |2 local 
655 7 |2 local 
786 0 |n Indonesian Journal of Electrical Engineering and Computer Science; Vol 21, No 3: March 2021; 1794-1799 
786 0 |n 2502-4760 
786 0 |n 2502-4752 
786 0 |n 10.11591/ijeecs.v21.i3 
787 0 |n https://ijeecs.iaescore.com/index.php/IJEECS/article/view/23393/14753 
856 4 1 |u https://ijeecs.iaescore.com/index.php/IJEECS/article/view/23393/14753  |z Get fulltext