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)


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.


Computational reading Arabic calligraphy Pattern recognition Natural language processing 



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


  1. 1.
    Sun, Y., Zhang, C., Huang, Z., Liu, J., Han, J., Ding, E.: TextNet: Irregular Text Reading from Images with an End-to-End Trainable Network, pp. 1–17 (2018)Google Scholar
  2. 2.
    Azmi, A., Alsaiari, A.: Arabic typography: a survey. Int. J. Electr. Comput. Sci. 9(10), 16–22 (2010)Google Scholar
  3. 3.
    Bataineh, B., Norul, S., Sheikh, H., Omar, K.: Generating an arabic calligraphy text blocks for global texture analysis. Int. J. Adv. Sci. Eng. Inf. Technol. 1, 150–155 (2011)CrossRefGoogle Scholar
  4. 4.
    Saberi, A., et al.: Evaluating the legibility of decorative Arabic scripts for Sultan Alauddin mosque using an enhanced soft-computing hybrid algorithm. Comput. Human Behav. 55, 127–144 (2016)CrossRefGoogle Scholar
  5. 5.
    Mohamed, N.A., Youssef, K.T.: Arts and design studies utilization of arabic calligraphy to promote the arabic identity in packaging designs. Arts Des. Stud. 19, 35–49 (2014)Google Scholar
  6. 6.
    Bataineh, B., Abdullah, S.N.H.S., Omar, K.: A novel statistical feature extraction method for textual images: Optical font recognition. Expert Syst. Appl. 39(5), 5470–5477 (2012)CrossRefGoogle Scholar
  7. 7.
    Aburas, A.A., Gumah, M.E.: Arabic handwriting recognition : challenges and solutions electrical and computer engineering dept international islamic university malaysia department of information technology, university technology PETRONAS 2. Pervious related Research Work. In: International Symposium on Information Technology, pp. 1–6 (2008)Google Scholar
  8. 8.
    Bhowmik, S., Sarkar, R., Nasipuri, M., Doermann, D.: Text and non-text separation in offline document images: a survey. Int. J. Doc. Anal. Recognit. 21(1–2), 1–20 (2018)CrossRefGoogle Scholar
  9. 9.
    Nagy, G.: Training a calligraphy style classifier on a non-representative training set. Electron. Imag. 2016(17), 1–8 (2017)Google Scholar
  10. 10.
    Jiulong, Z., Luming, G., Su, Y., Sun, X., Li, X.: Detecting Chinese calligraphy style consistency by deep learning and one-class SVM. In: 2017 2nd International Conference Image, Vis. Computer ICIVC 2017, pp. 83–86 (2017)Google Scholar
  11. 11.
    Ye, Q., Doermann, D.: Text detection and recognition in imagery: a survey. IEEE Trans. Pattern Anal. Mach. Intell. 37(7), 1480–1500 (2015)CrossRefGoogle Scholar
  12. 12.
    Rabi, M., Amrouch, M., Mahani, Z.: A survey of contextual handwritten recognition systems based HMMs for cursive arabic and latin script. Int. J. Comput. Appl. 160(2), 31–37 (2017)Google Scholar
  13. 13.
    Azmi, M.S., Omar, K., Nasrudin, M.F., Wan Mohd Ghazali, K., Abdullah, A.: Arabic calligraphy identification for digital jawi paleography using triangle blocks. In: Proceedings 2011 International Conference Electronic Engineering Informatics, ICEEI 2011, July, pp. 1–5 (2011)Google Scholar
  14. 14.
    Bataineh, B., Abdullah, S.N.H.S., Omar, K.: Arabic calligraphy recognition based on binarization methods and degraded images. In: Proceedings 2011 International Conference Pattern Analysis Intelligence Robotics. ICPAIR 2011, vol. 1, pp. 65–70 (2011)Google Scholar
  15. 15.
    Azmi, M.S., Omar, K., Nasrudin, M.F., Muda, A.K., Abdullah, A.: Arabic calligraphy classification using triangle model for Digital Jawi Paleography analysis. In: Proceedings 2011 11th International Conference Hybrid Intelligence System, HIS 2011, pp. 704–708 (2011)Google Scholar
  16. 16.
    AlSalamah, S., King, R.: Towards the machine reading of arabic calligraphy: a letters dataset and corresponding corpus of text. In: 2nd IEEE Workshop on Arabic and Derived Script Analysis and Recognition, ASAR 2018, pp. 19–23 (2018)Google Scholar
  17. 17.
    Bin, P.G., Bin Talal, M.: Free Islamic Calligraphy (2012).
  18. 18.
    Makandar, A., Halalli, B.: Image enhancement techniques using highpass and lowpass filters. Int. J. Comput. Appl. 109(14), 21–27 (2015)Google Scholar
  19. 19.
    Chen, Q., Sen Sun, Q., Ann Heng, P., Shen Xia, D.: A double-threshold image binarization method based on edge detector. Pattern Recognit. 41(4), 1254–1267 (2008)CrossRefGoogle Scholar
  20. 20.
    Gui, Y., Bai, X., Li, Z., Yuan, Y.: Color image segmentation using mean shift and improved spectral clustering. EURASIP J. Adv. Signal Process. 2012(December), 5–7 (2012)Google Scholar
  21. 21.
    Lampert, C.H., Blaschko, M.B., Hofmann, T.: Beyond sliding windows: object localization by efficient subwindow search. In: 26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR (2008)Google Scholar
  22. 22.
    Bay, H., Ess, A., Tuytelaars, T., Vangool, L.: Speeded-Up robust features (SURF) (Cited by: 2272). Comput. Vis. Image Underst. 110(3), 346–359 (2008)CrossRefGoogle Scholar
  23. 23.
    Dalal, N., Triggs, B., Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection to cite this version: histograms of oriented gradients for human detection. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. 1, 886–893 (2005)Google Scholar
  24. 24.
    Haroon, M.: Comparative analysis of stemming algorithms for web text mining. Int. J. Mod. Educ. Comput. Sci. 10(9), 20–25 (2018)MathSciNetCrossRefGoogle Scholar
  25. 25.
    Nguyen, N.T.H., Soto, A.J., Kontonatsios, G., Batista-Navarro, R., Ananiadou, S.: Constructing a biodiversity terminological inventory. PLoS ONE 12(4), 1–23 (2017)Google Scholar
  26. 26.
    Zhang, J., Guo, M., Fan, J.: A novel CNN structure for fine-grained classification of Chinese calligraphy styles. Int. J. Doc. Anal. Recognit. 22(2), 177–188 (2019)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of ManchesterManchesterUK

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