A New Computational Method for Arabic Calligraphy Style Representation and Classification
Source abstract
Abstract
Despite the importance of recognizing Arabic calligraphy styles and their potential usefulness for many applications, a very limited number of Arabic calligraphy style recognition works have been established. Thus, we propose a new computational tool for Arabic calligraphy style recognition (ACSR). The present work aims to identify Arabic calligraphy style (ACS) from images where text images are captured by different tools from different resources. To this end, we were inspired by the indices used by human experts to distinguish different calligraphy styles. These indices were transformed into a descriptor that defines, for each calligraphy style, a set of specific features. Three scenarios have been considered in the experimental part to prove the effectiveness of the proposed tool. The results confirmed the outperformance of both individual and combine features coded by our descriptor. The proposed work demonstrated outstanding performance, even with few training samples, compared to other related works for Arabic calligraphy recognition.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Image Retrieval and Classification Techniques · Handwritten Text Recognition Techniques · Advanced Image and Video Retrieval Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Lab or Artificial Intelligence and Data Science, Kasdi Merbah Ouargla University, Ouargla BP. 511, Algeria
LAMIA Laboratory, University du Québec à Trois-Rivières, Trois-Rivières, QC G8Z 4M3, Canada
LINATI Laboratory, University of Ouargla-Kasdi Merbah, Ouargla BP. 511, Algeria
References (33)
Show source-provided references
1. Document Layout Analysis: A Comprehensive Survey
2. Dornberger, R. (2020). A Survey of State of the Art Methods Employed in the Offline Signature Verification Process. New Trends in Business Information Systems and Technology: Digital Innovation and Digital Business Transformation, Springer Science and Business Media LLC.
3. Writer identification using machine learning approaches: A comprehensive review10.1007/s11042-018-6577-1 ↗
4. (2021, May 10). Available online: https://www.worldatlas.com/articles/the-world-s-most-popular-writing-scripts.html.
5. Fast learning neural network based on texture for Arabic calligraphy identification
6. Arabic and Farsi Font Recognition: Survey10.1142/S021800141553002X ↗
7. Bar Yosef, I., Kedem, K., Dinstein, I., Beit-Arie, M., and Engel, E. (2004, January 23–24). Classification of hebrew calligraphic handwriting styles: Preliminary results. Proceedings of the First International Workshop on Document Image Analysis for Libraries, Palo Alto, CA, USA.
8. Artistic features extraction from chinese calligraphy works via regional guided filter with reference image10.1007/s11042-017-4688-8 ↗
9. Chinese calligraphic style representation for recognition10.1007/s10032-016-0277-z ↗
10. Arabic Font Recognition Based on Discret Curvelet Transform10.1007/978-3-319-60834-1_36 ↗
11. A novel Arabic font recognition system based on texture feature and dynamic training
12. Jaiem, F.K., Slimane, F., and Kherallah, M. (2017, January 17–19). Arabic font recognition system applied to different text entity level analysis. Proceedings of the 2017 International Conference on Smart, Monitored and Controlled Cities (SM2C) 2017, Sfax, Tunisia.10.1109/SM2C.2017.8071847 ↗
13. Sakr, G., Mhanna, A., and Demerjian, R. (2019, January 26–29). Convolution Neural Networks for Arabic Font Recognition. Proceedings of the 2019 15th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS), Sorento, Italy.10.1109/SITIS.2019.00031 ↗
14. Deep Arabic Font Family and Font Size Recognition
15. Kaoudja, Z., Khaldi, B., and Kherfi, M.L. (2020, January 16–17). Arabic Artistic Script Style Identification Using Texture Descriptors. Proceedings of the 2020 1st International Conference on Communications, Control Systems and Signal Processing (CCSSP), El Oued, Algeria.10.1109/CCSSP49278.2020.9151569 ↗
16. A novel statistical feature extraction method for textual images: Optical font recognition10.1016/j.eswa.2011.11.078 ↗
17. Bataineh, B., Abdullah, S.N.H.S., Omar, K., and Batayneh, A. (2013, January 28–29). Arabic-Jawi Scripts Font Recognition Using First-Order Edge Direction Matrix. Proceedings of the International Multi-Conference on Artificial Intelligence Technology, Shah Alam, Malaysia.10.1007/978-3-642-40567-9_3 ↗
18. Bataineh, B., Abdullah, S.N.H.S., and Omar, K. (2011, January 28–29). Arabic calligraphy recognition based on binarization methods and de-graded images. Proceedings of the 2011 International Conference on Pattern Analysis and Intelligent Robotics, ICPAIR 2011, Putrajaya, Malaysia.10.1109/ICPAIR.2011.5976913 ↗
19. Generating an Arabic Calligraphy Text Blocks for Global Texture Analysis10.18517/ijaseit.1.2.33 ↗
20. Talab, M.A., Abdullah, S.N.H.S., and Razalan, M.H.A. (2013, January 15–18). Edge direction matrixes-based local binary patterns descriptor for invariant pattern recognition. Proceedings of the 2013 International Conference on Soft Computing and Pattern Recognition (SoCPaR), Hanoi, Vietnam.10.1109/SOCPAR.2013.7054123 ↗
21. Azmi, M.S., Omar, K., Nasrudin, M.F., Ghazali, K.W.M., Abdullah, A., and Abdullah, A. (2011, January 17–19). Arabic calligraphy identification for Digital Jawi Paleography using triangle blocks. Proceedings of the 2011 International Conference on Electrical Engineering and Informatics, Bandung, Indonesia.10.1109/ICEEI.2011.6021785 ↗
22. Azmi, M.S., Omar, K., Nasrudin, M.F., Muda, A.K., and Abdullah, A. (2011, January 5–8). Arabic calligraphy classification using triangle model for Digital Jawi Paleography analysis. Proceedings of the 2011 11th International Conference on Hybrid Intelligent Systems (HIS), Malacca, Malaysia.10.1109/HIS.2011.6122194 ↗
23. Adam, K., Al-Maadeed, S., and Bouridane, A. (2017, January 3–5). Letter-based classification of Arabic scripts style in ancient Arabic manuscripts: Preliminary results. Proceedings of the 2017 1st International Workshop on Arabic Script Analysis and Recognition (ASAR), Nancy, France.10.1109/ASAR.2017.8067767 ↗
24. Automatic Recognition of Artistic Arabic Calligraphy Types
25. Deep learning autoencoder approach: Automatic recognition of artistic Arabic calligraphy types
26. A Deep Learning Based Prediction of Arabic Manuscripts Handwriting Style
27. Kaoudja, Z., Kherfi, M.L., and Khaldi, B. (2019, January 26–27). An efficient multiple-classifier system for Arabic calligraphy style recognition. Proceedings of the 2019 International Conference on Networking and Advanced Systems (ICNAS), Annaba, Algeria.10.1109/ICNAS.2019.8807829 ↗
28. Moghaddam, R.F., Cheriet, M., Milo, T., and Wisnovsky, R. (2012, January 2–5). A prototype system for handwritten sub-word recognition: Toward Arabic-manuscript transliteration. Proceedings of the 2012 11th International Conference on Information Science, Signal Processing and their Applications (ISSPA), Montreal, QC, Canada.10.1109/ISSPA.2012.6310473 ↗
29. A threshold selection method from gray-level histograms10.1109/TSMC.1979.4310076 ↗
30. Cheriet, M., Kharma, N., Liu, C.-L., and Suen, C. (2007). Character Recognition Systems: A Guide for Students and Practitioners, John Wiley & Sons.10.1002/9780470176535 ↗
31. Arabic font recognition based on diacritics features10.1016/j.patcog.2013.07.015 ↗
32. Visual pattern recognition by moment invariants10.1109/TIT.1962.1057692 ↗
33. Combining colour and grey-level co-occurrence matrix features: A comparative study10.1049/iet-ipr.2018.6440 ↗