Ghost Character Recognition Theory and Arabic Script Based Languages Character Recognition

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. Streszczenie. Jezyki arabskie są bardzo trudne do zaadaptowania w systemie automatycznego rozpoznawania znakow. W artykule opisano algorytm Ghost character umozliwiający realizacje OCR wiekszości jezykow arabskich. (Algorytm Ghost character w zastosowaniu do rozpoznawania znakow jezyka arabskiego)

[1]  A. Dehghani,et al.  Off-line recognition of isolated Persian handwritten characters using multiple hidden Markov models , 2001, Proceedings International Conference on Information Technology: Coding and Computing.

[2]  Ramzi A. Haraty,et al.  Segmenting Handwritten Arabic Text , 2002 .

[3]  Robert M. Haralick,et al.  Segmentation-free word recognition with application to Arabic , 1995, Proceedings of 3rd International Conference on Document Analysis and Recognition.

[4]  Mokhtar Sellami,et al.  Off-line handwritten Arabic character segmentation algorithm: ACSA , 2002, Proceedings Eighth International Workshop on Frontiers in Handwriting Recognition.

[5]  A. O. Abd El-Gwad,et al.  Automatic recognition of handwritten Arabic characters , 1993 .

[6]  Murray J. J. Holt,et al.  Recognition of Off-Line Cursive Handwriting , 1998, Comput. Vis. Image Underst..

[7]  Robert M. Haralick,et al.  A segmentation-free approach to text recognition with application to Arabic text , 1996, International Journal on Document Analysis and Recognition.

[8]  Chafic Mokbel,et al.  Arabic handwriting recognition using baseline dependant features and hidden Markov modeling , 2005, Eighth International Conference on Document Analysis and Recognition (ICDAR'05).

[9]  Volker Märgner,et al.  HMM based approach for handwritten arabic word recognition using the IFN/ENIT - database , 2003, Seventh International Conference on Document Analysis and Recognition, 2003. Proceedings..

[10]  P. Adibi,et al.  NASTAALIGH HANDWRITTEN WORD RECOGNITION USING A CONTINUOUS-DENSITY VARIABLE-DURATION HMM , 2005 .

[11]  Mokhtar Sellami,et al.  Rule Based Neural Networks Construction for Handwritten Arabic City-Names Recognition , 2004, AIMSA.

[12]  Somaya Al-Máadeed,et al.  A data base for Arabic handwritten text recognition research , 2002, Proceedings Eighth International Workshop on Frontiers in Handwriting Recognition.

[13]  Muhammad Sher,et al.  HMM and fuzzy logic: A hybrid approach for online Urdu script-based languages' character recognition , 2010, Knowl. Based Syst..

[14]  Gilbert Lazard,et al.  THE RISE OF THE NEW PERSIAN LANGUAGE , 1975 .

[15]  Najoua Essoukri Ben Amara,et al.  Planar Markov modeling for Arabic writing recognition: advancement state , 2001, Proceedings of Sixth International Conference on Document Analysis and Recognition.

[16]  Ramzi A. Haraty,et al.  Arabic Text Recognition , 2004, Int. Arab J. Inf. Technol..

[17]  Mohammad S. Khorsheed,et al.  Recognising handwritten Arabic manuscripts using a single hidden Markov model , 2003, Pattern Recognit. Lett..