Calliar: an online handwritten dataset for Arabic calligraphy

Calligraphy is an essential part of the Arabic heritage and culture. It has been used in the past for the decoration of houses and mosques. Usually, such calligraphy is designed manually by experts with aesthetic insights. In the past few years, there has been a considerable effort to digitize such...

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Bibliographic Details
Published in:Neural computing & applications 2022-12, Vol.34 (23), p.20701-20713
Main Authors: Alyafeai, Zaid, Al-shaibani, Maged S., Ghaleb, Mustafa, Al-Wajih, Yousif Ahmed
Format: Article
Language:English
Subjects:
Artificial Intelligence
Calligraphy
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer Science
Data Mining and Knowledge Discovery
Datasets
Handwriting
Image Processing and Computer Vision
Mosques
Original Article
Probability and Statistics in Computer Science
Sentences
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Summary:Calligraphy is an essential part of the Arabic heritage and culture. It has been used in the past for the decoration of houses and mosques. Usually, such calligraphy is designed manually by experts with aesthetic insights. In the past few years, there has been a considerable effort to digitize such type of art by either taking a photograph of decorated buildings or drawing them using digital devices. The latter is considered an online form where the drawing is tracked by recording the apparatus movement, an electronic pen, for instance, on a screen. In the literature, there are many offline datasets with diverse Arabic styles for calligraphy. However, there is no available online dataset for Arabic calligraphy. In this paper, we illustrate our approach for collecting and annotating an online dataset for Arabic calligraphy called Calliar, which consists of 2,500 sentences. Calliar is annotated for stroke, character, word, and sentence-level prediction. We also propose various baseline models for the character classification task. The results we achieved highlight that it is still an open problem.
ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-022-07537-2