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Using Prior Knowledge to Facilitate Computational Reading of Arabic Calligraphy

  • Seetah ALSalamah
  • Riza Batista-NavarroEmail author
  • Ross D. KingEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11872)

Abstract

Arabic calligraphy (AC) is central to Arabic cultural heritage and has been used since its introduction, with the first writing of the Holy Quran, up until the present. It is famous for the artistic and complicated ways that letters and words interweave and intertwine to express textual statements – usually quotations from the Quran. These specifications make it probably the hardest of all human writing systems to read. Here, we introduce the challenge of reading Arabic calligraphy using artificial intelligence (AI), a challenge that combines image processing and understanding of texts. We have collected a corpus of 1000 AC images along with annotated quotations from the Quran, pre-processing the images and identifying individual letters using detection methods based on maximally stable extremal regions (MSERs) and sliding windows (SWs). We then collect the identified letters to form bags of extracted letters (BOLs). These BOLs are then used to search for possible quotation from the corpus. Our results show that MSERs outperforms SWs in letter detection. Furthermore, BOL-matching is better than word generation in predicting the correct quotation, with the correct answer found in the list of 10 topmost matches for more than 74% of the 388 test examples.

Keywords

Computational reading Arabic calligraphy Pattern recognition Natural language processing 

Notes

Acknowledgement

SA would like to thank King Saud University for funding this research.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of ManchesterManchesterUK

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