Multi-Font Farsi/Arabic Isolated Character Recognition Using Chain Codes
Nowadays, OCR systems have got several
applications and are increasingly employed in daily life. Much
research has been done regarding the identification of Latin,
Japanese, and Chinese characters. However, very little investigation
has been performed regarding Farsi/Arabic characters recognition.
Probably the reason is difficulty and complexity of those characters
identification compared to the others and limitation of IT activities in
Farsi and Arabic speaking countries. In this paper, a technique has
been employed to identify isolated Farsi/Arabic characters. A chain
code based algorithm along with other significant peculiarities such
as number and location of dots and auxiliary parts, and the number of
holes existing in the isolated character has been used in this study to
identify Farsi/Arabic characters. Experimental results show the
relatively high accuracy of the method developed when it is tested on
several standard Farsi fonts.
[1] M. M. Altuwaijri and M. A.Bayoumi, "Arabic text recognition using
neural networks", IEEE International Symposium on Circuits and
Systems, pp;415-418, 1994.
[2] B.M.F.Bushofa and M.Spann, "Segmentation and recognition of Arabic
characters by structural classification",Image and Vision Computing, ,
15,pp:167-179, 1997.
[3] B. Al-Badr and S. A. Mahmoud, "Survey and bibliography of Arabic
optical text recognition", Signal Processing, 41, pp:49-77, 1995.
[4] L. Zheng, Abbas H. Hassin and X.Tang, "A new algorithm for machine
printed Arabic character segmentation", Pattern Recognition Characters,
25(15),pp:1723-1729, 2004.
[5] J. Mantas, "An Overview of Character Recognition Methodologies",
Pattern Recognition 19, pp. 425-430, 1986.
[6] R. M. Bozinovic and S. N. Shihari, "Off Line Cursive Script Word
Recognition", IEEE Trans.Pattern Anal. Mach. Intell. PAMI 11, pp. 68-
83, 1989.
[7] R. Casey and G. Nagy, "Automatic Reading Machine", IEE Trans.
Comput. 17, pp. 492-503, 1968.
[8] K. Y. Wang, R. C. Casey and F. M. Wahl, "Document Analysis
System", IBM J.Res.Dev . 26, pp. 647-656, 1982.
[9] S. Mori, C. Y. Suen and K. Yamamoto, "Historical Review of OCR
Research and Development", Proc. IEEE 80, pp. 1029-1058, 1992.
[10] N. B. Amor, N. E. BenAmara, "Multifont Arabic Characters Recognition
Using Hough Transform and HMM/ANN Classification", journal of
multimedia, VOL. 1, NO. 2, MAY 2006.
[11] Lippmann "Pattern Classification using Neural Networks." IEEE
Communications Magazine, 1989.
[12] Kavianifar M. and Amin A. "Preprocessing and structural feature
extraction for a multi-fonts Arabic/Persian OCR", Proceedings of the
Fifth International Conference on Document Analysis and Recognition.
ICDAR '99 (Cat. No.PR00318). Soc, Los Alamitos, CA, USA. pp: 213-
16, 1999.
[13] S. Alma'adeed, C. Higgens, D. Elliman, R. Kasturi, D. Laurendeau, and
C. Suen, "Recognition of off-line handwritten Arabic words using
hidden Markov model approach" , Proceedings 16th International
Conference on Pattern Recognition.IEEE Comput. Soc, Los Alamitos,
CA, USA. Vol.3: 481-4, 2002.
[14] J. Cowell, F. Hussain, M. H. Hamza, and M. Sarfraz, "Extracting
features from Arabic characters", Proceedings of the IASTED erence
Computer Graphics and Imaging. ACTA Press, Anaheim, CA, USA, pp:
201-6, 2001.
[15] T. Sari, and M. Sellami, "MOrpho-LEXical analysis for correcting OCRgenerated
Arabic words" , Proceedings Eighth International Workshop
on Frontiers in Handwriting Recognition. IEEE Comput. Soc, Los
Alamitos, CA, USA. pp: 461-6, 2002.
[16] S. Hoque, K. Sirlantzis, M. C. Fairhurst, "A New Chain-code
Quantization Approach Enabling High Performance Handwriting
Recognition based on Multi-Classifier Schemes", Proceedings of the
Seventh International Conference on Document Analysis and
Recognition , ICDAR 2003.
[17] T. Y. Zhang, C. Y. Suen, "A fast parallel algorithm for thinning digital
patterns", ACM 1984.
[18] Pratt, K. William, "Digital Image Processing", New York, John Wiley &
Sons, Inc., p. 633, 1991.
[19] P. Zingaretti, M. Casparroni and L. Vecci, "Fast chain coding of region
boundarie", IEEE Transactions on Pattern Analysis and Machine
Intelligence 20 (4), 407-415, 1998.
[20] Christopher J.C. Burges, "A Tutorial on Support Vector Machines for
Pattern Recognition", Data Mining and Knowledge Discovery, Vol.2,
No.2, 1998.
[21] L. Almeida, "Multilayer Perceptrons", Handbook of Neural
Computation, IOP Publishing Ltd and Oxford University Press, pp.
C1.2: 1-C1.2: 30, 1997.
[22] T. Mitchell, "Machine Learning", McGraw Hill, New York, 1997.
[1] M. M. Altuwaijri and M. A.Bayoumi, "Arabic text recognition using
neural networks", IEEE International Symposium on Circuits and
Systems, pp;415-418, 1994.
[2] B.M.F.Bushofa and M.Spann, "Segmentation and recognition of Arabic
characters by structural classification",Image and Vision Computing, ,
15,pp:167-179, 1997.
[3] B. Al-Badr and S. A. Mahmoud, "Survey and bibliography of Arabic
optical text recognition", Signal Processing, 41, pp:49-77, 1995.
[4] L. Zheng, Abbas H. Hassin and X.Tang, "A new algorithm for machine
printed Arabic character segmentation", Pattern Recognition Characters,
25(15),pp:1723-1729, 2004.
[5] J. Mantas, "An Overview of Character Recognition Methodologies",
Pattern Recognition 19, pp. 425-430, 1986.
[6] R. M. Bozinovic and S. N. Shihari, "Off Line Cursive Script Word
Recognition", IEEE Trans.Pattern Anal. Mach. Intell. PAMI 11, pp. 68-
83, 1989.
[7] R. Casey and G. Nagy, "Automatic Reading Machine", IEE Trans.
Comput. 17, pp. 492-503, 1968.
[8] K. Y. Wang, R. C. Casey and F. M. Wahl, "Document Analysis
System", IBM J.Res.Dev . 26, pp. 647-656, 1982.
[9] S. Mori, C. Y. Suen and K. Yamamoto, "Historical Review of OCR
Research and Development", Proc. IEEE 80, pp. 1029-1058, 1992.
[10] N. B. Amor, N. E. BenAmara, "Multifont Arabic Characters Recognition
Using Hough Transform and HMM/ANN Classification", journal of
multimedia, VOL. 1, NO. 2, MAY 2006.
[11] Lippmann "Pattern Classification using Neural Networks." IEEE
Communications Magazine, 1989.
[12] Kavianifar M. and Amin A. "Preprocessing and structural feature
extraction for a multi-fonts Arabic/Persian OCR", Proceedings of the
Fifth International Conference on Document Analysis and Recognition.
ICDAR '99 (Cat. No.PR00318). Soc, Los Alamitos, CA, USA. pp: 213-
16, 1999.
[13] S. Alma'adeed, C. Higgens, D. Elliman, R. Kasturi, D. Laurendeau, and
C. Suen, "Recognition of off-line handwritten Arabic words using
hidden Markov model approach" , Proceedings 16th International
Conference on Pattern Recognition.IEEE Comput. Soc, Los Alamitos,
CA, USA. Vol.3: 481-4, 2002.
[14] J. Cowell, F. Hussain, M. H. Hamza, and M. Sarfraz, "Extracting
features from Arabic characters", Proceedings of the IASTED erence
Computer Graphics and Imaging. ACTA Press, Anaheim, CA, USA, pp:
201-6, 2001.
[15] T. Sari, and M. Sellami, "MOrpho-LEXical analysis for correcting OCRgenerated
Arabic words" , Proceedings Eighth International Workshop
on Frontiers in Handwriting Recognition. IEEE Comput. Soc, Los
Alamitos, CA, USA. pp: 461-6, 2002.
[16] S. Hoque, K. Sirlantzis, M. C. Fairhurst, "A New Chain-code
Quantization Approach Enabling High Performance Handwriting
Recognition based on Multi-Classifier Schemes", Proceedings of the
Seventh International Conference on Document Analysis and
Recognition , ICDAR 2003.
[17] T. Y. Zhang, C. Y. Suen, "A fast parallel algorithm for thinning digital
patterns", ACM 1984.
[18] Pratt, K. William, "Digital Image Processing", New York, John Wiley &
Sons, Inc., p. 633, 1991.
[19] P. Zingaretti, M. Casparroni and L. Vecci, "Fast chain coding of region
boundarie", IEEE Transactions on Pattern Analysis and Machine
Intelligence 20 (4), 407-415, 1998.
[20] Christopher J.C. Burges, "A Tutorial on Support Vector Machines for
Pattern Recognition", Data Mining and Knowledge Discovery, Vol.2,
No.2, 1998.
[21] L. Almeida, "Multilayer Perceptrons", Handbook of Neural
Computation, IOP Publishing Ltd and Oxford University Press, pp.
C1.2: 1-C1.2: 30, 1997.
[22] T. Mitchell, "Machine Learning", McGraw Hill, New York, 1997.
@article{"International Journal of Information, Control and Computer Sciences:49444", author = "H. Izakian and S. A. Monadjemi and B. Tork Ladani and K. Zamanifar", title = "Multi-Font Farsi/Arabic Isolated Character Recognition Using Chain Codes", abstract = "Nowadays, OCR systems have got several
applications and are increasingly employed in daily life. Much
research has been done regarding the identification of Latin,
Japanese, and Chinese characters. However, very little investigation
has been performed regarding Farsi/Arabic characters recognition.
Probably the reason is difficulty and complexity of those characters
identification compared to the others and limitation of IT activities in
Farsi and Arabic speaking countries. In this paper, a technique has
been employed to identify isolated Farsi/Arabic characters. A chain
code based algorithm along with other significant peculiarities such
as number and location of dots and auxiliary parts, and the number of
holes existing in the isolated character has been used in this study to
identify Farsi/Arabic characters. Experimental results show the
relatively high accuracy of the method developed when it is tested on
several standard Farsi fonts.", keywords = "Farsi characters, OCR, feature extraction, chain
code.", volume = "2", number = "7", pages = "2305-4", }
