A DATASET FOR THE DIACRITICS IMAGES OF THE HOLY QURAN: TOWARDS A TACTILE-VISION SYSTEM FOR THE VISUALLY IMPAIRED
DOI:
https://doi.org/10.30572/2018/KJE/150305Keywords:
Vision Sensory Substitution (VSS), Optical Character Recognition (OCR), Image Dataset, Holy Quran, Dots and DiacriticsAbstract
The only way for the Blind and visually impaired persons to read the holy Quran is by using a special paper edition of embossed Braille code. A new approach to reading the Holy Quran text for those persons was proposed by the author. One of the main demands for this approach is the classification of the dots and diacritics, which can be abbreviated as DaDs. This paper outlines the creation of a dataset of images for the DaDs of Al-Mushaf. A handheld scanner was developed for this purpose, and MATLAB programs were employed for DaDs segmentation. The final goal is the design of a tactile vision sensory substitution system based on an optical character recognition technique to help blind individuals read the Holy Quran in an alternative way to Braille codes. Approximately 1750 images were taken from two distinct Al-Mushaf versions with the proposed handheld scanner. Using the suggested techniques and algorithms, 6000 DaDs were retrieved from these images; however, only 4710 images of the DaDs, arranged in 22 classes, were selected after the repeated, incomplete, and non-DaDs were eliminated. The dataset was organized by DaD class, prepared to be used directly for machine learning purposes, and made available for public use upon request.
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Abdallah Abualkishik and Khairuddin Omar (2009) ‘Quranic Braille System’, International Journal of Computer, Electrical, Automation, Control and Information Engineering, 2(10), pp. 3306–3312. Available at: http://www.waset.org/publications/7971.
Abualkishik, A. and Omar, K. (2013) ‘Framework for translating the Holy Quran and its reciting rules to Braille code’, International Conference on Research and Innovation in Information Systems, ICRIIS, 2013, pp. 380–385. doi: 10.1109/ICRIIS.2013.6716740.
Abualkishik, A. M. and Omar, K. (2009) ‘Quran Vibrations in Braille Code Department of System Science and Management’, in International Conference on Electrical Engineering and Informatics, pp. 12–17.
Alheraki, M., Al-Matham, R. and Al-Khalifa, H. (2023) ‘Handwritten Arabic Character Recognition for Children Writing Using Convolutional Neural Network and Stroke Identification’, Human-Centric Intelligent Systems. Springer Netherlands, 3(2), pp. 147–159. doi: 10.1007/s44230-023-00024-4.
Al-Shatnawi, A. and Omar, K. (2008) ‘Methods of Arabic Language Baseline Detection – The State of Art’, Journal of Computer Science, 8(10), pp. 137–143.
Altwaijry, N. and Al-Turaiki, I. (2021) ‘Arabic handwriting recognition system using convolutional neural network’, Neural Computing and Applications. Springer London, 33(7), pp. 2249–2261. doi: 10.1007/s00521-020-05070-8.
Bach-y-Rita, P. and W. Kercel, S. (2003) ‘Sensory substitution and the human–machine interface’, Trends in Cognitive Sciences, 7(12), pp. 541–546. doi: 10.1016/j.tics.2003.10.013.
Bach-y-Rita, P., Collins, C. C., Saunders, F. A., White, B., & Scadden, L. (1969). Vision substitution by tactile image projection. Nature, 221(5184), 963-964.
Bach-y-Rita, P., Tyler, M. E. and Kaczmarek, K. A. (2003) ‘Seeing with the brain’, International Journal of Human-Computer Interaction, 15(2), pp. 285–295.
doi: 10.1207/S15327590IJHC1502_6.
Boubaker, H., Chaabouni, A., Halima, M. B., El Baati, A., & El Abed, H. (2014, August). Arabic diacritics detection and fuzzy representation for segmented handwriting graphemes modeling. In 2014 6th International Conference of Soft Computing and Pattern Recognition (SoCPaR) (pp. 71-76). IEEE.
Boubaker, H., Tagougui, N., El Abed, H., Kherallah, M., & Alimi, A. M. (2014). Graphemes segmentation for arabic online handwriting modeling. Journal of information processing systems, 10(4), 503-522.
Elkhayati, M., Elkettani, Y. and Mourchid, M. (2022) ‘Segmentation of Handwritten Arabic Graphemes Using a Directed Convolutional Neural Network and Mathematical Morphology Operations’, Pattern Recognition. Elsevier Ltd, 122, p. 108288.
doi: 10.1016/j.patcog.2021.108288.
Eraqi, H. M. and Abdelazeem, S. (2012) ‘A new efficient graphemes segmentation technique for offline Arabic handwriting’, in Proceedings - International Workshop on Frontiers in Handwriting Recognition, IWFHR, pp. 95–100. doi: 10.1109/ICFHR.2012.162.
Eraqi, H. M. and Azeem, S. A. (2011) ‘An on-line Arabic handwriting recognition system: Based on a new on-line graphemes Segmentation Technique’, in Proceedings of the International Conference on Document Analysis and Recognition, ICDAR, pp. 409–413.
doi: 10.1109/ICDAR.2011.90.
Fadel, A., Tuffaha, I., & Al-Ayyoub, M. (2019, May). Arabic text diacritization using deep neural networks. In 2019 2nd international conference on computer applications & information security (ICCAIS) (pp. 1-7). IEEE.
Kaczmarek, K. A., Webster, J. G., Bach-y-Rita, P., & Tompkins, W. J. (1991). Electrotactile and vibrotactile displays for sensory substitution systems. IEEE transactions on biomedical engineering, 38(1), 1-16.
Kef, M., Chergui, L. and Chikhi, S. (2016) ‘A novel fuzzy approach for handwritten Arabic character recognition’, Pattern Analysis and Applications, 19(4), pp. 1041–1056.
doi: 10.1007/s10044-015-0500-4.
Madhfar, M. A. H. and Qamar, A. M. (2021) ‘Effective Deep Learning Models for Automatic Diacritization of Arabic Text’, IEEE Access, 9, pp. 273–288.
doi: 10.1109/ACCESS.2020.3041676.
Maghraby, A. and Samkari, E. (2023) ‘Arabic Text Recognition with Harakat Using Deep Learning’, International Journal of Computer Science and Network Security (IJCSNS), 23(1), pp. 41–45.
Mohammed, A. (2023) ‘Arabic / English Handwritten Digits Recognition using MLPs ’, Al-Rafidain Engineering Journal (AREJ), 28(2), pp. 252–260.
Mohd, M., Qamar, F., Al-Sheikh, I., & Salah, R. (2021). Quranic optical text recognition using deep learning models. IEEE Access, 9, 38318-38330.
Mousa, M., Sayed, M. and Abdalla, I. (2013) ‘Arabic Character Segmentation Using Projection-Based Approach with Profile’ s Amplitude Filter’, International Conference of Information and Communication Technology (ICoICT), pp. 122–126.
Nazih, W. and Hifny, Y. (2022) ‘Arabic Syntactic Diacritics Restoration Using BERT Models’, Computational Intelligence and Neuroscience, 2022(1), p. 8 pages. doi: 10.1155/2022/3214255.
Rubini, R. and Setyawan, C. E. (2020) ‘Inclusion Education: Learning Reading Arabic Language And Alquran For Blind’, Al-Bidayah: Jurnal Pendidikan Dasar Islam, 11(2), pp. 330–345. doi: 10.14421/al-bidayah.v11i2.348.
Sulaiman, S., Rambli, D. R. A. and Zuki, F. S. M. (2015) ‘Putting the tactile feedback to Quranic verses and tajweed rules’, ARPN Journal of Engineering and Applied Sciences, 10(23), pp. 17996–18003.
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