Deep learning autoencoder approach: Automatic recognition of artistic Arabic calligraphy types

Rami Al-Hmouz*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)

Abstract

Recognition of Arabic calligraphy types is a challenging problem. Difficulties include similarities among different types, overlap between letters, and letters that assume different shapes. In this study, a deep learning approach to recognizing artistic Arabic calligraphy types is presented. Autoencoder is a deep learning approach with the capability of reducing data dimensions in addition to extract features. Autoencoders can be stacked with several layers. The system is composed of three layers consisting of two encoder layers to extract features and one soft max layer for the recognition stage. The font can be recognized in a collective manner based on the words or segments that exist in the font images. The input of the system consists of individual words or segment images that compose the font image, and the output is the recognized font type. The approach was evaluated on local and public datasets, and the achieved recognition rates were 92.1% and 99.5%, respectively.

Original languageEnglish
Pages (from-to)2-14
Number of pages13
JournalKuwait Journal of Science
Volume47
Issue number3
Publication statusPublished - 2020
Externally publishedYes

Keywords

  • Artistic Arabic calligraphy
  • Autoencoder
  • Deep learning
  • Optical font recognition

ASJC Scopus subject areas

  • General

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