Multi-Font Farsi/Arabic Isolated Character Recognition Using Chain Codes

Authors: H. Izakian, S. A. Monadjemi, B. Tork Ladani, K. Zamanifar

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.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1328082

References:

[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.