This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
Style Consistent Classification of Isogenous Patterns
January 2005 (vol. 27 no. 1)
pp. 88-98
Prateek Sarkar, IEEE Computer Society
In many applications of pattern recognition, patterns appear together in groups (fields) that have a common origin. For example, a printed word is usually a field of character patterns printed in the same font. A common origin induces consistency of style in features measured on patterns. The features of patterns co-occurring in a field are statistically dependent because they share the same, albeit unknown, style. Style constrained classifiers achieve higher classification accuracy by modeling such dependence among patterns in a field. Effects of style consistency on the distributions of field-features (concatenation of pattern features) can be modeled by hierarchical mixtures. Each field derives from a mixture of styles, while, within a field, a pattern derives from a class-style conditional mixture of Gaussians. Based on this model, an optimal style constrained classifier processes entire fields of patterns rendered in a consistent but unknown style. In a laboratory experiment, style constrained classification reduced errors on fields of printed digits by nearly 25 percent over singlet classifiers. Longer fields favor our classification method because they furnish more information about the underlying style.

[1] P.J. Bickel and K.A. Doksum, Mathematical Statistics: Basic Ideas and Selected Topics. Englewood Cliffs, N.J.: Prentice Hall, 1977.
[2] T. Breuel and C. Mathis, “Classification Using a Hierarchical Bayesian Approach,” Proc. 16th Int'l Conf. Pattern Recognition, pp. 40103-40106, Aug. 2002.
[3] H.S. Baird and G. Nagy, “A Self-Correcting 100-Font Classifier,” Document Recognition, Proc. SPIE, L. Vincent and T. Pavlidis, eds., vol. 2181, pp. 106-115, 1994.
[4] I. Bazzi, R. Schwartz, and J. Makhoul, “An Omnifont Open-Vocabulary OCR System for English and Arabic,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 21, no. 6, pp. 495-504, June 1999.
[5] V. Bouletreau, N. Vincent, R. Sabourin, and H. Emptoz, “Synthetic Parameters for Handwriting Classification,” Proc. Fourth Int'l Conf. Document Analysis and Recognition, vol. 1, pp. 102-106, 1997.
[6] R.G. Casey, “Text OCR by Solving a Cryptogram,” Proc. Eighth Int'l Conf. Pattern Recognition, pp. 349-351, 1986.
[7] R.O. Duda and P.E. Hart, Pattern Classification and Scene Analysis. New York: John Wiley and Sons, 1973.
[8] R.O. Duda, P.E. Hart, and D.G. Stork, Pattern Classification. New York: John Wiley and Sons, 2001.
[9] A.P. Dempster, M.M. Laird, and D.B. Rubin, “Maximum Likelihood from Incomplete Data via the EM Algorithm,” J. Royal Statistical Soc., vol. 39, no. 1, pp. 1-38, 1977.
[10] T.K. Ho, J.J. Hull, and S.N. Srihari, “Word Recognition with Multi-Level Contextual Knowledge,” Proc. First Int'l Conf. Document Analysis and Recognition, pp. 905-915, Oct. 1991.
[11] J.J. Hull and S.N. Srihari, “Experiments in Text Recognition with Binary N-Gram and Viterbi Algorithms,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 4, no. 5, pp. 520-530, Sept. 1982.
[12] F. Jelinek, Statistical Methods in Speech Recognition. Cambridge, Mass.: MIT Press, 1997.
[13] G. Nagy, “Teaching a Computer to Read,” Proc. 11th Int'l Conf. Pattern Recognition, vol. 2, pp. 225-229, Sept. 1992.
[14] K.A. Nathan, J.R. Bellegarda, D. Nahamoo, and E.J. Bellegarda, “On-Line Handwriting Recognition Using Continuous Parameter Hidden Markov Models,” Proc. Int'l Conf. Acoustics, Speech, and Signal Processing, vol. 5, pp. 121-124, 1993.
[15] R. Plamondon, D.P. Lopresti, L.R.B. Schomaker, and R. Srihari, “On-Line Handwriting Recognition,” Wiley Encyclopedia of Electrical and Electronics Eng., J.G. Webster, ed. pp. 123-146, New York: John Wiley & Sons, 1999.
[16] P. Sarkar, “Style Consistency in Pattern Fields,” PhD thesis, Rensselaer Polytechnic Inst., 2000.
[17] P. Sarkar, “An Iterative Algorithm for Optimal Style-Conscious Field Classification,” Proc. 16th Int'l Conf. Pattern Recognition, vol. IV, pp. 243-246, Aug. 2002.
[18] P. Sarkar, H.S. Baird, and X. Zhang, “Training on Severely Degraded Text-Line Images,” Proc. Seventh Int'l Conf. Document Analysis and Recognition, pp. 38-43, Aug. 2003.
[19] P. Sarkar and G. Nagy, “Classification of Style-Constrained Pattern-Fields,” Proc. 15th Int'l Conf. Pattern Recognition, pp. 859-862, 2000.
[20] P. Sarkar and G. Nagy, “Style Consistency in Isogenous Patterns,” Proc. Sixth Int'l Conf. Document Analysis and Recognition, pp. 1169-1174, Sept. 2001.
[21] G. Schwarz, “Estimating the Dimension of a Model,” Annals of Statistics, vol. 6, no. 2, pp. 461-464, 1978.
[22] R.M.K. Sinha and B. Prasada, “Visual Text Recognition through Contextual Processing,” Pattern Recognition, vol. 20, no. 5, pp. 463-479, 1988.
[23] J.B. Tenenbaum and W.T. Freeman, “Separating Style and Content,” Advances in Neural Information Processing Systems 9, M. C. Mozer, M.I. Jordan, and T. Petsche, eds., San Mateo, Calif.: Morgan Kaufmann, 1997.
[24] S. Veeramachaneni and G. Nagy, “Style Context with Second-Order Statistics,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 27, no. 1, pp. 14-22, Jan. 2005.
[25] S. Veeramachaneni and G. Nagy, “Adaptive Classifiers for Multisource OCR,” Int'l J. Document Analysis and Recognition, vol. 6, no. 3, pp. 154-166, Aug. 2004.
[26] S. Veeramachaneni and G. Nagy, “Style-Conscious Quadratic Classifier,” Proc. 16th Int'l Conf. Pattern Recognition, vol. II, pp. 72-75, Aug. 2002.
[27] S. Veeramachaneni, G. Nagy, C.-L. Liu, and H. Fujisawa, “Classifying Isogenous Fields,” Proc. Eighth Int'l Workshop Frontiers of Handwriting Recognition, pp. 41-46, Aug. 2002.
[28] Y. Xu and G. Nagy, “Prototype Extraction and Adaptive OCR,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 21, no. 12, pp. 1280-1296, Dec. 1999.
[29] A. Zramdini and R. Ingold, “Optical Font Recognition Using Typographical Features,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 20, no. 8, pp. 877-882, Aug. 1998.

Index Terms:
Style, isogenous patterns, style consistency, style constrained classification, style-bound variant, style-shared variant, Optical Character Recognition, font recognition, field classification, mixture model.
Citation:
Prateek Sarkar, George Nagy, "Style Consistent Classification of Isogenous Patterns," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 1, pp. 88-98, Jan. 2005, doi:10.1109/TPAMI.2005.18
Usage of this product signifies your acceptance of the Terms of Use.