과제정보
We would like to thank the manuscripts department at King Abdullah Library at Umm Al-Qura University for providing their expertise.
참고문헌
- A. Shinde and D. Chougule, "Text pre-processing and text segmentation for OCR," International Journal of Computer Science Engineering and Technology, vol. 2, no. 1, pp. 810-812, 2012.
- A. Priya, S. Mishra, S. Raj, S. Mandal, and S. Datta, "Online and offline character recognition: A survey," in 2016 International Conference on Communication and Signal Processing (ICCSP), pp. 0967-0970, IEEE, 2016.
- A. Zafar and A. Iqbal, "Application of soft computing techniques in machine reading of Quranic Kufic manuscripts," Journal of King Saud University- Computer and Information Sciences, 2020.
- K. Adam, S. Al-Maadeed, and A. Bouridane, "Letter-based classification of Arabic scripts style in ancient Arabic manuscripts: Preliminary results," in 2017 1st International Workshop on Arabic Script Analysis and Recognition (ASAR), pp. 95-98, IEEE, 2017.
- Z. Kaoudja, M. L. Kherfi, and B. Khaldi, "An efficient multiple-classifier system for Arabic calligraphy style recognition," in 2019 International Conference on Networking and Advanced Systems (ICNAS), pp. 1-5, IEEE, 2019.
- M. Elmansouri, N. E. Makhfi, and B. Aghoutane, "Toward classification of arabic manuscripts words based on the deep convolutional neural networks," in 2020 International Conference on Intelligent Systems and Computer Vision (ISCV), pp. 1-5, IEEE, 2020.
- B. Alrehali, N. Alsaedi, H. Alahmadi, and N. Abid, "Historical Arabic manuscripts text recognition using convolutional neural network," in 2020 6th Conference on Data Science and Machine Learning Applications (CDMA), pp. 37-42, IEEE, 2020.
- R. Alaasam, B. Kurar, M. Kassis, and J. El-Sana, "Experiment study on utilizing convolutional neural networks to recognize historical Arabic hand- written text," in 2017 1st International Workshop on Arabic Script Analysis and Recognition (ASAR), pp. 124-128, IEEE, 2017.
- R. Alaasam, B. K. Barakat, and J. El-Sana, "Synthesizing versus augmentation for Arabic word recognition with convolutional neural networks," in 2018 IEEE 2nd International Workshop on Arabic and Derived Script Analysis and Recognition (ASAR), pp. 114-118, IEEE, 2018.
- H. Boukerma and N. Farah, "Preprocessing algorithms for Arabic handwriting recognition systems," in 2012 International Conference on Advanced Computer Science Applications and Technologies (ACSAT), pp. 318-323, IEEE, 2012.
- L. Dounas, M. I. Azzouzi, and N. Rais, "New algorithm for the transcription of Arabic manuscripts," in 2012 Colloquium in Information Science and Technology, pp. 86-90, IEEE, 2012.
- M. S. Khorsheed, "Recognising handwritten Arabic manuscripts using a single hidden Markov model," Pattern Recognition Letters, vol. 24, no. 14, pp. 2235-2242, 2003. https://doi.org/10.1016/S0167-8655(03)00050-3
- N. E. makhfi, "Handwritten Arabic word spotting using speeded up robust features algorithm," in 2019 5th International Conference on Optimization and Applications (ICOA), pp. 1-6, IEEE, 2019.
- E. Chammas, C. Mokbel, and L. Likforman-Sulem, "Arabic handwritten document preprocessing and recognition," in 2015 13th International Conference on Document Analysis and Recognition (ICDAR), pp. 451-455, IEEE, 2015.
- B. Shi, X. Bai, and C. Yao, "An end-to-end trainable neural network for image-based sequence recognition and its application to scene text recognition," IEEE transactions on pattern analysis and machine intelligence, vol. 39, no. 11, pp. 2298-2304, 2016.
- X. Feng, Z. Wang, and T. Liu, "Port container number recognition system based on improved yolo and CRNN algorithm," in 2020 International Conference on Artificial Intelligence and Electromechanical Automation (AIEA), pp. 72-77, IEEE, 2020.
- L. Zhao and K. Jia, "Application of CRNN based OCR in health records system," in Proceedings of the 3rd International Conference on Multimedia Systems and Signal Processing, pp. 46-50, 2018.
- R. Achkar, K. Ghayad, R. Haidar, S. Saleh, and R. Al Hajj, "Medical hand- written prescription recognition using CRNN," in 2019 International Conference on Computer, Information and Telecommunication Systems (CITS), pp. 1-5, IEEE, 2019.
- L. Chen and S. Li, "Improvement research and application of text recognition algorithm based on CRNN," in Proceedings of the 2018 International Conference on Signal Processing and Machine Learning, pp. 166-170, 2018.
- M. Elleuch, N. Tagougui, and M. Kherallah, "Arabic handwritten characters recognition using deep belief neural networks," in 2015 IEEE 12th International MultiConference on Systems, Signals & Devices (SSD15), pp. 1-5, IEEE, 2015.
- Giurgiu, I., Riva, O., Juric, D., Krivulev, I., Alonso, G.: Calling the Cloud: Enabling Mobile Phones as Interfaces to Cloud Applications. In: Bacon, J.M., Cooper, B.F. (eds.) Middleware 2009. LNCS, vol. 5896, pp. 83-102. Springer, Heidelberg (2009).