PL EN


Preferences help
enabled [disable] Abstract
Number of results
2019 | 115 | 160-173
Article title

To recognize the manuscript texts of Arabic letters in ancient Uzbek script

Content
Title variants
Languages of publication
EN
Abstracts
EN
This article describes the Hemming method of the neural model for automatic identification of Arabic texts on the computer. The main problem of recognizing manuscript mantles in Arabic is that of the elements that they have created. Usually, the text is divided into rows, and then separated by separate words. The development of the Arabic language signifies a great deal of controversy over Arabic language. Hemming is based on the neuronal model and the description of the software product.
Year
Volume
115
Pages
160-173
Physical description
Contributors
  • ICT Center under the Tashkent University of Information Technology named after Muhammad al-Kharazmiy, Tashkent City, Uzbekistan
References
  • [1] S. Young, al., the HTK Book V3.4 Cambridge University Press, Cambridge UK, 2006 M. T. Parvez, A. M. Sabri, Arabic handwriting recognition using structural and syntactic pattern attributes. Pattern Recognition 46 (2013) 141-154.
  • [2] M. Azmi and R. S. Almajed. A survey of automatic Arabic diacritization techniques. Natural Lang. Eng. 21(3) 2013) 477–495.
  • [3] S. Ananthakrishnan, S. Narayanan, and S. Bangalore. Automatic diacritization of arabic transcripts for automatic speech recognition. Proc. 4th Int. Conf. Natural Lang. Process. Kanpur, India, 2005, pp. 1–8.
  • [4] M. Elshafie, H. Almuhtaseb, and M. Alghamdi. Techniques for high quality Arabic speech synthesis. Inf. Sci. vol. 140, pp. 255–267, Feb. 2002.
  • [5] Rebai and Y. BenAyed. Text-to-speech synthesis system with Arabic diacritic recognition system. Comput. Speech Lang. vol. 34, no. 1, pp. 43–60, 2015.
  • [6] Zouaghi, L. Merhbene, and M. Zrigui, Combination of information retrieval methods with LESK algorithm for Arabic word sense disambiguation. Artif. Intell. Rev. vol. 38, no. 4, pp. 257–269, 2017.
  • [7] Farghaly and K. Shaalan. Arabic natural language processing: Challenges and solutions. ACM Trans. Asian Lang. Inf. Process vol. 8, no. 4, pp. 1–22, 2009
  • [8] M. Maamouri and A. Bies. The Penn Arabic Treebank. Proc. Arabic Comput. Linguistics, Stanford, CA, USA, 2010, pp. 1–42.
  • [9] O. Bahanshal and H. S. Al-Khalifa. A first approach to the evaluation of Arabic diacritization systems. Proc. 7th Int. Conf. Digit. Inf. Manage. (ICDIM), Macau, China, Aug. 2012, pp. 155–158
  • [10] Y. A. El-Imam. Phonetization of Arabic: rules and algorithms. Comput. Speech Lang. vol. 18, no. 4, pp. 339–373, 2003.
  • [11] M. Attia. A large-scale computational processor of the Arabic morphology, and applications. M.S thesis, Faculty Eng., Cairo Univ., Giza, Egypt, 2000.
  • [12] M. Attia. Handling Arabic morphological and syntactic ambiguity within the LFG framework with a view to machine translation. Ph.D. dissertation, School Lang., Linguistics Culture, Univ. Manchester, Manchester, U.K., 2008.
  • [13] M. Badrashiny. Automatic diacritizer for Arabic texts. M.S. thesis, Faculty Eng., Cairo Univ., Giza, Egypt, 2009.
  • [14] G. Abandah, A. Graves, B. Al-Shagoor, A. Arabiyat, F. Jamour, and M. Al-Taee, Automatic diacritization of Arabic text using recurrent neural networks. Proc. 13th Int. Conf. Document Anal. Recognit. Nancy, France, vol. 18, 2015, pp. 183–197.
  • [15] Y. Gal. An HMM approach to vowel restoration in Arabic and Hebrew. Proc. ACL Workshop Comput. Approaches Semitic Lang., 2002, pp. 27–33.
  • [16] A. El-Harby, M. A. El-Shehawey, and R. El-Barogy. A statistical approach for Qur’an vowel restoration. J. Artif. Intell. Mach. Learn. vol. 8, no. 3, pp. 9–16, 2008.
  • [17] M. S. Khorsheed. A HMM-based system to diacritize Arabic text. J. Softw. Eng. Appl. vol. 5, pp. 124–127, Dec. 2017.
  • [18] M. Elshafei, H. Al-Muhtaseb, and M. Alghamdi. Statistical methods for automatic diacritization of Arabic text. Proc. 18th Nat. Comput. Conf., Riyadh, Saudi Arbia, 2006, pp. 301–306.
  • [19] M. Elshafei, H. Al-Muhtaseb, and M. Alghamdi. Machine generation of Arabic diacritical marks. Proc. Int. Conf. Mach. Learn., Models, Technol. Appl., Las Vegas, NV, USA, 2006, pp. 128–133.
  • [20] R. Nelken and S. M. Shieber. Arabic diacritization using weighted finitestate transducers. Proc. ACL Workshop Comput. Approaches Semitic Lang., 2005, pp. 79–86.
  • [21] Y. Hifny. Higher order n-gram language models for Arabic diacritics restoration. Proc. 12th Conf. Lang. Eng. (ESOLEC), Cairo, Egypt, 2012, pp. 1–5.
  • [22] Zitouni, J. Sorensen, and R. Sarikaya. Maximum entropy based restoration of Arabic diacritics. Proc. 21st Int. Conf. Comput. Linguistics 44th Annu. Meeting ACL, Sydney, NSW, Australia, 2006, pp. 577–584.
  • [23] Zitouni and R. Sarikaya. Arabic diacritic restoration approach based on maximum entropy models. Comput. Speech Lang. vol. 23, no. 3, pp. 257–276, 2009.
  • [24] M. Alghamdi et al., Automatic Arabic text diacritizer. King Abdulaziz City Sci. Technol., Riyadh, Saudi Arabia, Tech. Rep. CI.25.02, 2006.
  • [25] P. De Doncker. A volume/surface potential formulation of the method of moments applied to electromagnetic scattering. Eng. Anal. Boundary Elements, vol. 27, no. 4, pp. 325–331, 2003.
  • [26] F. Vico, L. Greengard, M. Ferrando, and Z. Gimbutas. The decoupled potential integral equation for time-harmonic electromagnetic scattering. Commun. Pure Appl. Math. vol. 69, no. 4, pp. 771–812, Apr. 2016.
  • [27] W. C. Chew. Vector potential electromagnetics with generalized gauge for inhomogeneous media: Formulation (invited paper). Prog. Electromagn. Res. vol. 149, pp. 69–84, 2014.
  • [28] Q. S. Liu, S. Sun, and W. C. Chew. A vector potential integral equation method for electromagnetic scattering. Proc. Int. Rev. Prog. Appl. Comput. Electromagn. (ACES), Mar. 2015, pp. 1–2.
  • [29] D. Jackson, Classical Electrodynamics. San Rafael, CA, USA: Morgan & Claypool, 2008.
  • [30] O. Ergül and L. Gürel, The Multilevel Fast Multipole Algorithm (MLFMA) for Solving Large-Scale Computational Electromagnetics Problems. New York, NY, USA: Wiley, 2014.
  • [31] M. Benzi, G. H. Golub, and J. Liesen. Numerical solution of saddle point problems. Acta Nemurica vol. 14, pp. 1–137, Apr. 2005.
  • [32] C. Keller, N. I. M. Gould, and A. J. Wathen. Constraint preconditioning for indefinite linear systems. SIAM J. Matrix Anal. Appl. vol. 21, no. 4, pp. 1300–1317, 2000.
  • [33] R. S. Varga, Geršgorin and His Circles. Berlin, Germany: Springer-Verlag, 2004.
  • [34] Anne-Laure Bianne-Bernard, Fares Menasri, Laurence Likforman-sulem, Chafic Mokbel, Christopher Kermorvant (2012) Variable length and context-dependent HMM letter form models for Arabic handwritten word recognition. In Document Recognition and Retrieval Conference (DRR)
  • [35] AL-Shatnawi, M. Atallah, AL-Salaimeh, Safwan, AL-Zawaideh, Farah Hanna, Omar, Khairuddin, Offline arabic text recognition an overview. World Comput. Sci. Inform. Technol. J. 1 (5), 184–192, 2011.
  • [36] H. Alkhateeb, O. Pauplin, J. Ren, J. Jiang, Performance of hidden Markov model and dynamic Bayesian network classifiers on handwritten Arabic word recognition. Knowledge-Based Systems 24 (2011), pp. 680-688.
  • [37] W. Chew, M.-S. Tong, and B. Hu, Integral Equation Methods for Electromagnetic and Elastic Waves. San Rafael, CA, USA: Morgan & Claypool, 2008.
  • [38] F. P. Andriulli, A. Tabacco, and G. Vecchi, Solving the EFIE at low frequencies with a conditioning that grows only logarithmically with the number of unknowns. Antennas Propag. vol. 58, no. 5, pp. 1614–1624, May 2010.
  • [39] Lawgali. A Survey on Arabic Character Recognition. International Journal of Signal Processing, Image Processing and Pattern Recognition Vol. 8, No. 2 (2015), pp. 401-426.
Document Type
article
Publication order reference
Identifiers
YADDA identifier
bwmeta1.element.psjd-3dc8c353-f59d-4d3d-825e-df966264d723
JavaScript is turned off in your web browser. Turn it on to take full advantage of this site, then refresh the page.