Support Vector Machine for Persian Font Recognition
In this paper we examine the use of global texture analysis based approaches for the purpose of Persian font recognition in machine-printed document images. Most existing methods for font recognition make use of local typographical features and connected component analysis. However derivation of such features is not an easy task. Gabor filters are appropriate tools for texture analysis and are motivated by human visual system. Here we consider document images as textures and use Gabor filter responses for identifying the fonts. The method is content independent and involves no local feature analysis. Two different classifiers Weighted Euclidean Distance and SVM are used for the purpose of classification. Experiments on seven different type faces and four font styles show average accuracy of 85% with WED and 82% with SVM classifier over typefaces
[1] B. B. Chaudhuri, and U. Garain, "Automatic Detection of Italic, Bold
and All-Capital words in Document Images", Proc. of 14th International
Conference on Pattern Recognition - ICPR, Brisbane (Australia),
pp.610-612, 1998.
[2] B. B. Chaudhuri, and U. Garain, "Extraction of type style-based metainformation
from imaged documents", International Journal on
Document Analysis and Recognition - IJDAR, vol. 3, no. 3, pp. 138-149,
2001.
[3] K. Y. Wong, R. G. Casey, and F. M. Wahl, "Document Analysis
System", IBM Journal of Research and Development, vol. 26, no. 6, pp.
647-656, 1982.
[4] V. Eglin, S. Bres, and H. Emptoz, "Statistical characterization and
classification of printed text in a multiscale context", Proc. of 2nd
International Workshop on Statistical Techniques in Pattern
Recognition - SPR-98, Sydney (NSW), (Australia), pp.960-967, 1998.
[5] L. Duffy, "Recherche d'information logique dans les documents à
typographie riche et récurrente. Application aux sommaires", Thesis of
the Institut National des Sciences Appliquées - INSA de Lyon, Lyon
(France), 160p, 1997.
[6] A. Zramdini, "Study of Optical Font Recognition Based on Global
Typographical Features", Thesis of the University of Fribourg, Fribourg
(Switzerland), 170p, 1995.H. Poor, An Introduction to Signal Detection
and Estimation. New York: Springer-Verlag, 1985, ch. 4.
[7] D. S. Doermann, E. Rivlin, and A. Rosenfeld, "The function of
documents", International Journal of Computer Vision - IJCV, pp. 799-
814, 1998.E. H. Miller, "A note on reflector arrays (Periodical styleÔÇö
Accepted for publication)," IEEE Trans. Antennas Propagat., to be
published.
[8] V.Wu, R. Manmatha, and E. M. Riseman, "Finding Text in Images",
Proc. of Second ACM International Conference on Digital Libraries,
Philadelphia, PA (USA), pp.23-26, 1997.
[9] A. K. S. Jain, and K. Bhattacharjee, "Address Block Location on
Envelopes Using Gabor Filters", Pattern Recognition, vol. 25, no. 12,
pp. 1459-1477, 1992.
[10] A. K. S. Jain, K. Bhattacharjee, and Y. Chen, "On texture in document
images", Proc. Of IEEE Conference on Computer Vision and Pattern
Recognition, Champaign, Illinois (USA), pp.677-680, 1992.
[11] A. Bénédicte, H. Emptoz, "Font Type Extraction and Character
Prototyping Using Gabor Filters". International Conference on
Document Analysis and Recognition, p, 799-803, 2003.
[12] Y. Zhu, T. Tan, and Y. WANG, "Font Recognition Based On Global
Texture Analysis", IEEE Transactions on Pattern Analysis and Machine
Intelligence, vol. 23, no. 10, pp. 1192-1200, 2001.
[13] N. Otsu, "A threshold selection method for gray-level histogram", IEEE
Trans. On System Man Cybernetics, vol. SMC-9, no. 1, pp. 62-66, 1979.
[14] N. Petkov, "Biologically motivated computationally intensive
approaches to image pattern recognition", Future Gen. Comput. Syst.,
vol. 11, pp. 451-465, 1995.
[15] T. N. Tan, "Texture feature extraction via visual cortical channel
modeling", Proc. Of 11th IAPR Int. Conf. Pattern Recognition, vol. 3,
pp. 607-610, 1992.
[16] V. Vapnik, "The Nature of Statistical Learning Theory", Springer, 1995.
[17] C. J. C. Burges, "A tutorial on support vector machines for pattern
recognition", Data Mining and Knowledge Discovery, vol. 2, no.2, pp.
1-47, 1998.
[18] V. Vapnik, "Statistical learning theory". John Wiley and Sons, New
York, 1998.
[1] B. B. Chaudhuri, and U. Garain, "Automatic Detection of Italic, Bold
and All-Capital words in Document Images", Proc. of 14th International
Conference on Pattern Recognition - ICPR, Brisbane (Australia),
pp.610-612, 1998.
[2] B. B. Chaudhuri, and U. Garain, "Extraction of type style-based metainformation
from imaged documents", International Journal on
Document Analysis and Recognition - IJDAR, vol. 3, no. 3, pp. 138-149,
2001.
[3] K. Y. Wong, R. G. Casey, and F. M. Wahl, "Document Analysis
System", IBM Journal of Research and Development, vol. 26, no. 6, pp.
647-656, 1982.
[4] V. Eglin, S. Bres, and H. Emptoz, "Statistical characterization and
classification of printed text in a multiscale context", Proc. of 2nd
International Workshop on Statistical Techniques in Pattern
Recognition - SPR-98, Sydney (NSW), (Australia), pp.960-967, 1998.
[5] L. Duffy, "Recherche d'information logique dans les documents à
typographie riche et récurrente. Application aux sommaires", Thesis of
the Institut National des Sciences Appliquées - INSA de Lyon, Lyon
(France), 160p, 1997.
[6] A. Zramdini, "Study of Optical Font Recognition Based on Global
Typographical Features", Thesis of the University of Fribourg, Fribourg
(Switzerland), 170p, 1995.H. Poor, An Introduction to Signal Detection
and Estimation. New York: Springer-Verlag, 1985, ch. 4.
[7] D. S. Doermann, E. Rivlin, and A. Rosenfeld, "The function of
documents", International Journal of Computer Vision - IJCV, pp. 799-
814, 1998.E. H. Miller, "A note on reflector arrays (Periodical styleÔÇö
Accepted for publication)," IEEE Trans. Antennas Propagat., to be
published.
[8] V.Wu, R. Manmatha, and E. M. Riseman, "Finding Text in Images",
Proc. of Second ACM International Conference on Digital Libraries,
Philadelphia, PA (USA), pp.23-26, 1997.
[9] A. K. S. Jain, and K. Bhattacharjee, "Address Block Location on
Envelopes Using Gabor Filters", Pattern Recognition, vol. 25, no. 12,
pp. 1459-1477, 1992.
[10] A. K. S. Jain, K. Bhattacharjee, and Y. Chen, "On texture in document
images", Proc. Of IEEE Conference on Computer Vision and Pattern
Recognition, Champaign, Illinois (USA), pp.677-680, 1992.
[11] A. Bénédicte, H. Emptoz, "Font Type Extraction and Character
Prototyping Using Gabor Filters". International Conference on
Document Analysis and Recognition, p, 799-803, 2003.
[12] Y. Zhu, T. Tan, and Y. WANG, "Font Recognition Based On Global
Texture Analysis", IEEE Transactions on Pattern Analysis and Machine
Intelligence, vol. 23, no. 10, pp. 1192-1200, 2001.
[13] N. Otsu, "A threshold selection method for gray-level histogram", IEEE
Trans. On System Man Cybernetics, vol. SMC-9, no. 1, pp. 62-66, 1979.
[14] N. Petkov, "Biologically motivated computationally intensive
approaches to image pattern recognition", Future Gen. Comput. Syst.,
vol. 11, pp. 451-465, 1995.
[15] T. N. Tan, "Texture feature extraction via visual cortical channel
modeling", Proc. Of 11th IAPR Int. Conf. Pattern Recognition, vol. 3,
pp. 607-610, 1992.
[16] V. Vapnik, "The Nature of Statistical Learning Theory", Springer, 1995.
[17] C. J. C. Burges, "A tutorial on support vector machines for pattern
recognition", Data Mining and Knowledge Discovery, vol. 2, no.2, pp.
1-47, 1998.
[18] V. Vapnik, "Statistical learning theory". John Wiley and Sons, New
York, 1998.
@article{"International Journal of Information, Control and Computer Sciences:50728", author = "A. Borji and M. Hamidi", title = "Support Vector Machine for Persian Font Recognition ", abstract = "In this paper we examine the use of global texture analysis based approaches for the purpose of Persian font recognition in machine-printed document images. Most existing methods for font recognition make use of local typographical features and connected component analysis. However derivation of such features is not an easy task. Gabor filters are appropriate tools for texture analysis and are motivated by human visual system. Here we consider document images as textures and use Gabor filter responses for identifying the fonts. The method is content independent and involves no local feature analysis. Two different classifiers Weighted Euclidean Distance and SVM are used for the purpose of classification. Experiments on seven different type faces and four font styles show average accuracy of 85% with WED and 82% with SVM classifier over typefaces ", keywords = "Persian font recognition, support vector machine,gabor filter.", volume = "1", number = "4", pages = "861-4", }
