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Tytuł artykułu

Ghost Character Recognition Theory and Arabic Script Based Languages Character Recognition

Wybrane pełne teksty z tego czasopisma
Identyfikatory
Warianty tytułu
PL
Algorytm Ghost character w zastosowaniu do rozpoznawania znaków języka arabskiego
Języki publikacji
EN
Abstrakty
EN
Arabic script is used by more than 1/4th population of the world in the form of different languages like Arabic, Persian, Urdu, Sindhi, Pashto etc but each language have its own words meaning and set of alphabets. The set of Urdu alphabets is a superset of the alphabets sets for all other Arabic script based languages. Arabic script based languages character recognition is one of the most difficult task due to complexities involved in this script not exist in any other script. This paper present a novel technique Ghost Character Recognition Theory that will helps to develop a Multilanguage character recognition system for Arabic script based languages based on Ghost Character Theory. The main benefit of proposed approach is that it will works for all Arabic script based languages by doing little effort for ghost character (basic skeleton) and developing dictionary for every language. Handling all Arabic script based languages has several issues like recognition rate is low as compared to system for specific languages and specific writing style i.e. Nastaliq or Naskh, but in general, this small difference of recognition rate is not a big issue for multilingual system and at the end we will get multilingual character recognition system.
PL
Języki arabskie są bardzo trudne do zaadaptowania w systemie automatycznego rozpoznawania znaków. W artykule opisano algorytm Ghost character umożliwiający realizację OCR większości języków arabskich.
Rocznik
Strony
234--238
Opis fizyczny
Bibliogr. 19 poz., il., tabl., wykr.
Twórcy
autor
autor
  • International Islamic University, Islamabad, Pakistan and Information System Department, King Saud University, Saudi Arabiax, imranrazak@hotmail.com
Bibliografia
  • [1] Abuhaiba, M.J.J. Holt, and S. Datta, “Recognition of Off-Line Cursive Handwriting,” Computer Vision and Image Understanding, vol. 71, pp. 19-38, 1998.
  • [2] Attash Durani, "Pakistani: Lingual Aspect of National Integration of Pakistan", www.nlauit.gov.pk.
  • [3] Attash Durani, "Urdu Informatics" Vol. 1, pp. 102-112, pp 8-15, National Language Authority Press
  • [4] Dehghani, F. Shabani, and P. Nava, “Off-Line Recognition of Isolated Persian Handwritten Characters Using Multiple Hidden Markov Models,” Proceeding International Conference Information Technology: Coding and Computing, pp. 506-510, 2001.
  • [5] Al-Badr and R. Haralick, “A Segmentation-Free Approach to Text Recognition with Application to Arabic Text,” International Journal Document Analysis and Recognition, vol. 1, pp. 147- 166, 1998.
  • [6] Al-Badr and R. Haralick, “Segmentation-Free Word Recognition with Application to Arabic,” Proc. International Conference Document Analysis and Recognition, pp. 355-359, 1995.
  • [7] H. Miled and N.E. Ben Amara, “Planar Markov Modeling for Arabic Writing Recognition Advancement State,” Proc. International Conference Document Analysis and Recognition, pp. 69-73, 2001.
  • [8] Gilbert Lazard, “The Rise of the New Persian Language” in Frye, R. N., The Cambridge History of Iran, 1995, Vol. pp. 595– 632, Cambridge: Cambridge University Press.
  • [9] Souici, N. Farah, T. Sari, and M. Sellami, “Rule Based Neural Networks Construction for Handwritten Arabic City-Names Recognition,” Proceeding Artificial Intelligence: Methodology, Systems, and Applications, pp. 331-340, 2004.
  • [10] M..M. Fahmy and S. Al Ali, “Automatic Recognition of Handwritten Arabic Characters Using Their Geometrical Features,” Studies in Informatics and Control Journal., vol. 10, 2001.
  • [11] M.S. Khorsheed, “Recognising Handwritten Arabic Manuscripts Using a Single Hidden Markov Model,” Pattern Recognition Letters, vol. 24, pp. 2235-2242, 2003.
  • [12] M. Pechwitz and V. Ma¨rgner, “HMM Based Approach for Handwritten Arabic Word Recognition Using the IFN/ENITDatabase,” Proc. International Conference Document Analysis and Recognition, pp. 890-894, 2003.
  • [13] R. El-Hajj, L. Likforman-Sulem, and C. Mokbel, “Arabic Handwriting Recognition Using Baseline Dependant Features and Hidden Markov Modeling,” Proceeding International Conference Document Analysis and Recognition, pp. 893-897, 2005.
  • [14] M.I. Razzak , F. Anwar, S.A.Hussain, M. Sher, “Fuzzy and HMM: A Hybrid Approach for Urdu Script Based Languages Character Recognition” Knowledge Based System (Accepted), Elsewhere
  • [15] R. Safabakhsh and P. Adibi, “Nastaaligh Handwritten Word Recognition Using a Continuous-Density Variable-Duration HMM,” The Arabian Journal Science and Engineering., vol. 30, pp. 95-118, 2005.
  • [16] R. Haraty and A. Hamid, “Segmenting Handwritten Arabic Text,” Proceeding. Int. Conf. Computer Science, Software Eng., Information Technology, e-Business, and Applications, 2002.
  • [17] R. Haraty and C. Ghaddar, “Arabic Text Recognition,” International Arab Journal Information Technology, vol. 1, pp. 156-163, 2004.
  • [18] S. Alma’adeed, D. Elliman, and C.A. Higgins, “A Data Base for Arabic Handwritten Text Recognition Research,” Proceeding Eighth International Workshop Frontiers in Handwriting Recognition, pp. 485-489, 2002.
  • [19] T. Sari, L. Souici, and M. Sellami, “Off-Line Handwritten Arabic Character Segmentation Algorithm: ACSA,” Proc. International Workshop Frontiers in Handwriting Recognition, pp. 452-457, 2002.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-PWA7-0055-0024
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