The week's pick
Reseach Article
Using Box Approach in Persian Handwritten Digits Recognition
by
Omid Rashnodi,
Hedieh Sajedi,
Mohammad Saniee Abadeh
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 32 - Number 3 |
| Year of Publication: 2011 |
| Authors: Omid Rashnodi, Hedieh Sajedi, Mohammad Saniee Abadeh |
10.5120/3882-5428
|
Omid Rashnodi, Hedieh Sajedi, Mohammad Saniee Abadeh . Using Box Approach in Persian Handwritten Digits Recognition. International Journal of Computer Applications. 32, 3 ( October 2011), 1-8. DOI=10.5120/3882-5428
@article{
10.5120/3882-5428,
author = {
Omid Rashnodi,
Hedieh Sajedi,
Mohammad Saniee Abadeh
},
title = { Using Box Approach in Persian Handwritten Digits Recognition },
journal = {
International Journal of Computer Applications
},
issue_date = { October 2011 },
volume = { 32 },
number = { 3 },
month = { October },
year = { 2011 },
issn = { 0975-8887 },
pages = {
1-8
},
numpages = {9},
url = {
https://ijcaonline.org/archives/volume32/number3/3882-5428/
},
doi = { 10.5120/3882-5428 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:18:09.832340+05:30
%A Omid Rashnodi
%A Hedieh Sajedi
%A Mohammad Saniee Abadeh
%T Using Box Approach in Persian Handwritten Digits Recognition
%J International Journal of Computer Applications
%@ 0975-8887
%V 32
%N 3
%P 1-8
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract
In this paper, appropriate feature set based on the box approach has been proposed to achieve higher recognition accuracy and decreasing the recognition time of Persian numerals. In classification phase, support vector machine (SVM) with linear kernel has been employed as the classifier. Feature sets consists of 163 dimensions, which are the average angle and distance pixels which are equal to one in each box the box approach. The scheme has been evaluated on 60,000 handwritten samples of Persian numerals. Using 60,000 samples for training, scheme was tested on other 20,000 samples and 98.945% correct recognition rate was obtained.
References
- M. H. Shirali-Shahreza, K. Faze and A. Khotanzad, “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, pp. 436-439, 1992.
- C. Burges, “A Tutorial on support Vector machines for pattern recognition”, Data Mining & Knowledge Discovery, vol. 2, pp. 1- 43, 1998.
- H. Soltanzadeh and M. Rahmati, “Recognition of Persian handwritten digits using image profiles of multiple orientations”, Pattern Recognition Letters 25, pp. 1569–1576, 2004.
- V. N. Vapnik, “The Nature of Statistical Learning Theory”, Springer Verlang, 1995.
- M. Dehghan and K. Faez, “Farsi Handwritten Character Recognition with Moment Invariants”, Proceedings of 13th International Conference on Digital Signal Processing, vol. 2, pp. 507-510, 1997.
- S. N. Sridhar and G. Ball, "An Assessment of Arabic Handwriting Recognition Technology", CEDAR Technical Report TR-03-07, 2007.
- A. Harifi and A. Aghagolzadeh,” A New Pattern for Handwritten Persian/Arabic Digit Recognition”, Journal of Information Technology vol. 3, pp. 249-252, 2004.
- M. Ziaratban, K. Faez and F. Faradji “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, pp. 297-301, 2007.
- A. Mowlaei and K.Faez, “Recognition Of Isolated Handwritten Persian /Arabic Characters and Numerals Using Support Vector Machines”, Proceedings of XIII Workshop on Neural Networks for Signal Processing, pp. 547-554, 2003.
- H. Khosravi and E. Kabir,”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, pp. 1133-1141, 2007.
- H. Mir Mohammad Hosseini and A. Bouzerdoum,”A Combined Method for Persian and Arabic Handwritten Digit Recognition”, Australian New Zealand Conference on Intelligent Information System, pp. 80 – 83, 1996.
- A. Mowlaei, K. Faez ,A. Highlight, ”Feature Extraction with Wavelet Transform for Recognition of Isolated Handwritten Farsi/Arabic Characters and Numerals”, Digital Signal Processing vol. 2, pp. 923- 926, 2002.
- S. Mozaffari, K. Faez & H. Rashidy Kanan, “Recognition of Isolated Handwritten Farsi/Arabic Alphanumeric Using Fractal Codes”, Image Analysis and Interpretation, 6thSouthwest Symposium, pp. 104-108, 2004.
- S. Mozaffari, K. Faez and M. Ziaratban, “Structural Decomposition and Statistical Description of Farsi/Arabic Handwritten Numeric Characters”, Proceedings of the 8th Intl. Conference on Document Analysis and Recognition, vol. 1, pp. 237- 241, 2005.
- J. Sadri, C. Y. Suen and T. D. Bui, “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, pp. 300-307, 2003.
- V.N. Vapnik, “Principles of risk minimization for learning theory, Advances in Neural Information Processing Systems”, vol. 4, Morgan Kaufman, San Mateo, CA, pp. 831 – 838, 1992.
- V.N. Vapnik, “Statistical Learning Theory”, Wiley, New York, 1998.
- B.E. Boser, I. Guyon, V. Vapnik,” A training algorithm for optimal margin classifiers”, computes. Learn. Theory pp.144–152, 1992.
- N. Cristianini, J. Shawe-Taylor, “An Introduction to Support Vector Machines”, Cambridge University Press, Cambridge, 2000.
- B. Scholkopf, A.J. Smola,” Learning with Kernels”, MIT Press, Cambridge, MA, 2002.
- J. Shawe-Taylor, N. Cristianini, “Kernel Methods for Pattern Analysis”, Cambridge University Press, Cambridge, 2004.
- M. Hanmandlu, M.H.M. Yusof, M. Vamsi Krishna, “Off-line signature verification and forgery detection using fuzzy modeling”, Pattern Recognition vol. 3, no. 38, pp.341–356, 2005.
- M. Hanmandlu, K.R. Murali Mohan, S. Chakra borty, S. Goyal, D. Roy Choudhury, “Unconstrained handwritten character recognition based on fuzzy logic”, Pattern Recognition vol. 3, no. 36 , pp. 603–623, 2003.
- Burges, C.J.C., A tutorial on support vector machines for pattern recognition. Data Min. Know. Disc. 2, pp.121–167, 1998.
- Platt, J.C., 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, pp. 185–208. Chapter 12, 1999.
- N. Sharma, U. Pal and F. Kimura, “Recognition of Handwritten Kannada Numerals”, Proceedings of the 9th International Conference on Information Technology, vol. 1, pp. 133-136, 200
- J. Sadri, C. Y. Suen and T. D. Bui, “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, pp. 300-307, 2003.
- F. Friedrichs, C. Igel, Evolutionary tuning of multiple SVM parameters, in: M. Verleysen (Ed.), Proceedings of the 12th European Symposium on Artificial Neural Networks (ESANN), Bruges, Belgium, pp. 519–524, 2004.
- O. Chapelle, V.N. Vapnik, O. Bousquet, S. Mukherjee, Choosing multiple parameters for support vector machines, Mach. Learn pp. 131–159, 2002.
- N.E. Ayat, M. Cheriet, C.Y. Suen, Optimizing the SVM kernels using an empirical error minimization scheme, in: S. Lee, A. Verri (Eds.), Proceedings of the International Workshop on Pattern Recognition with Support Vector Machines, Lecture Notes in Computer Science, vol. 2388, pp. 354–369, 2002.
- M. Hanmandlu, O.V. Ramana Murthy, Fuzzy model based recognition of handwritten numerals, the journal of pattern recognition society, pp. 1840 – 1854, 2006.
Index Terms
Computer Science
Information Sciences
Keywords