The week's pick
Reseach Article
Persian Handwritten Digit Recognition using Support Vector Machines
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 29 - Number 12 |
| Year of Publication: 2011 |
| Authors: Omid Rashnodi, Hedieh Sajedi, Mohammad Saniee Abadeh |
10.5120/3702-5193
|
Omid Rashnodi, Hedieh Sajedi, Mohammad Saniee Abadeh . Persian Handwritten Digit Recognition using Support Vector Machines. International Journal of Computer Applications. 29, 12 ( September 2011), 1-6. DOI=10.5120/3702-5193
Abstract
In this paper, appropriate features set based on Discrete Fourier Transform coefficients and the box approach have been proposed to achieve higher recognition accuracy, decreasing the features set dimensions and recognition time of Persian numerals. In classification phase, support vector machine (SVM) has been employed as the classifier. Feature sets consists of 154 dimensions, which are the Fourier coefficients in the contour pixels of input image, average angle and distance pixels which are equal to one in each box the box approach. The scheme has been evaluated on 80,000 handwritten samples of Persian numerals. Using 60,000 samples for training, scheme was tested on other 20,000 samples and 98.84% correct recognition rate was obtained.
References
- S.N. Sridhar and G. Ball, 2007. "An Assessment of Arabic Handwriting Recognition Technology", CEDAR Technical Report TR-03-07.
- M.H. Shirali-Shahreza, K. Faze and A. Khotanzad, 1995. “Recognition of Hand-written Persian/Arabic Numerals by Shadow Coding and an Edited Probabilistic Neural Network“, Proceedings of International Conference on Image Processing, vol. 3, 436 – 439.
- A. Harifi and A. Aghagolzadeh, 2004.” A New Pattern for Handwritten Persian/Arabic Digit Recognition”, Journal of Information Technology, vol. 3, 249-252.
- H. Mir Mohammad Hosseini and A. Bouzerdoum, 1996.”A Combined Method for Persian and Arabic Handwritten Digit Recognition”, Australian New Zealand Conference on Intelligent Information System, 80 – 83.
- S. Mozaffari, K. Faez & H. Rashidy Kanan, 2004. “Recognition of Isolated Handwritten Farsi/Arabic Alphanumeric Using Fractal Codes”, Image Analysis and Interpretation, 6th Southwest Symposium, 104-108.
- H. Soltanzadeh and M. Rahmati, 2004. “Recognition of Persian handwritten digits using image profiles of multiple orientations”, Pattern Recognition Letters, vol. 25, 1569–1576.
- J. Sadri, C.Y. Suen and T.D. Bui, 2003. “Application of Support Vector Machines for Recognition of Handwritten Arabic/Persian Digits”, Proceedings of the 2nd Conference on Machine Vision and Image Processing & Applications, vol. 1, 300-307.
- M. Dehghan and K. Faez, 1997. “Farsi Handwritten Character Recognition With Moment Invariants”, Proceedings of 13th International Conference on Digital Signal Processing, vol. 2, 507-510.
- M. Ziaratban, K. Faez and F. Faradji, 2007. “Language-Based Feature Extraction Using Template-Matching in Farsi/Arabic Handwritten Numeral Recognition”, Proceedings of 9th International Conference on Document Analysis and Recognition, vol. 1, 297-301.
- S. Mozaffari, K. Faez and M. Ziaratban, 2005. “Structural Decomposition and Statistical Description of Farsi/Arabic Handwritten Numeric Characters”, Proceedings of the 8th Intl. Conference on Document Analysis and Recognition, vol. 1, 237- 241.
- A. Mowlaei and K. Faez, 2003. “Recognition Of Isolated Handwritten Persian /Arabic Characters and Numerals Using Support Vector Machines”, Proceedings of XIII Workshop on Neural Networks for Signal Processing, 547-554.
- A. Mowlaei, K. Faez, A. Highlight, 2002. ”Feature Extraction with Wavelet Transform for Recognition of Isolated Handwritten Farsi/Arabic Characters and Numerals”, Digital Signal Processing, vol. 2, 923- 926.
- M. Hanmandlu, M.H.M. Yusof, M. Vamsi Krishna, 2005. “Off-line signature verification and forgery detection using fuzzy modeling”, Pattern Recognition, vol. 38, No. 3, 341–356.
- M. Hanmandlu, K. R. Murali Mohan, S. Chakra borty, S. Goyal, D. Roy Choudhury, 2003. “Unconstrained handwritten character recognition based on fuzzy logic”, Pattern Recognition, vol. 36, No. 3, 603– 623.
- V.N. Vapnik, 1992. “Principles of risk minimization for learning theory, Advances in Neural Information Processing Systems”, vol. 4, Morgan Kaufman, San Mateo, CA, 831– 838.
- V.N. Vapnik, 1998. “Statistical Learning Theory”, Wiley, New York.
- B.E. Boser, I. Guyon, V. Vapnik, 1992.” A training algorithm for optimal margin classifiers”, Comput. Learn. Theory, 144¬¬ –152.
- N. Cristianini, J. Shawe-Taylor, 2000. “An Introduction to Support Vector Machines”, Cambridge University Press, Cambridge.
- B. Scholkopf, A.J. Smola,” Learning with Kernels”, 2002. MIT Press, Cambridge, MA.
- J. Shawe-Taylor, N. Cristianini, 2004. “Kernel Methods for Pattern Analysis”, Cambridge University Press, Cambridge.
- Burges, C.J.C., 1998. A tutorial on support vector machines for pattern recognition. Data Min. Know. Disc. 2, 121–167.
- Platt, J.C., 1999. Fast training of support vector machines using sequential minimal optimization. In: Scholkopf, B., Burges, C., Smola, A.J. (Eds.), Advances in Kernel Methods––Support Vector learning. MIT Press, Cambridge, Massa- chusetts, Chapter 12, 185–208.
- C. Burges, 1998. “A Tutorial on support Vector machines for pattern recognition”, Data Mining & Knowledge Discovery, vol. 2, 1- 43.
- V. N. Vapnik, 1995. “The Nature of Statistical Learning Theory”, Springer Verlang.
- H. Khosravi and E. Kabir, 2007.”Introducing a very large dataset of handwritten Farsi digits and a study on the variety of handwriting styles”, Pattern Recognition Letters, vol. 28, Issue 10, 1133-1141.
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