A MLP-SVM hybrid model for cursive handwriting recognition

Proceedings of the International Joint Conference on Neural Networks

Volumn , Issue , 2011, Pages 843-850

  Azevedo, Washington W ^a   Zanchet, Cleber ^a  

^a FEDERAL UNIVERSITY OF PERNAMBUCO   (Brazil)

Author keywords

[No Author keywords available]

Indexed keywords

SUPPORT VECTOR MACHINES; VECTOR SPACES;

HAND-WRITTEN CHARACTERS; HANDWRITING RECOGNITION; HYBRID ARCHITECTURES; INPUT PATTERNS; MAXIMUM OUTPUT; MULTI LAYER PERCEPTRON; RECOGNITION RATES; SUPPORT VECTOR MACHINE (SVMS);

CHARACTER RECOGNITION;

EID: 80054734774     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IJCNN.2011.6033309     Document Type: Conference Paper

References (53)

1. L. Bottou and V. Vapnik. Local learning algorithms. Neural Computation, 4(6):888-900, 1992.
2. Rahman AFR, Fairhurst MC (2000) Multiple expert classification: a new methodology for parallel decision fusion. International Journal on Document Analysis and Recognition 3:40-55.
3. K.Woods,W. P. K. Jr, and K. Bowyer. Combination of multiple classifiers using local accuracy estimates. IEEE Trans. on PAMI, 19(4):405-410, 1997.
4. Fechine, J.; Madeiro, F.; Neto, B. G. A. Reconhecimento automático da identidade vocal baseado em quantização vetorial e HMM (In Portuguese). 2da. Conferencia Iberoamericana en Sistemas, Cibernética e informática - CISCI, 2003.
5. Yacoubi, E. et al. An HMM-based approach for off-line unconstrained handwritten word modelling and recognition. IEE Transaction on Pattern Analysis and Machine Intelligence, v. 21, n. 8, p. 752-760, 1999.
6. V. N. Vapnik. An overview of statistical learning theory. IEEE Trans. on Neural Networks, 10(5):988-999, 1999.
7. Trier, O.D.; Jains, A. K.; Taxt, T. Feature extraction methods for character recognition - a survey. Pattern Recognition, v. 29, n. 4, p. 641-662, 1996.
8. R. A. Jacobs, M. I. Jordan, S. J. Nowlan, and G. E. Hinton. Adaptive mixtures of local experts. Neural Computation, 3(1):79-87, 1991.
9. B. E. Boser, I.M. Guyon, and V. N. Vapnik. A training algorithm for optimal margin classifiers. In Proc. Fifth Annual Workshop on Comput. Learn. Theory, Pittsburgh, USA, July 1992.
10. Huang YS, Suen CY (1995) A Method of Combining Multiple Experts for the Recognition of Unconstrained Handwritten Numerals. IEEE Transactions on Pattern Analysis and Machine Intelligence 17(1):90-94.
11. J. Kittler. Combining classifiers: A theoretical framework. Pattern Analysis and Applic., 1(1):18-27, 1998.
12. Rabiner, L. R. A tutorial on hidden markov models and selected applications in speech recognition. Proceedings of the IEEE, v. 77, n. 2, p. 257-289, 1989.
13. Francesco Camastra, "A svm-based cursive character recognizer,"Pattern Recognition, vol. 40, no. 12, pp. 3721-3727, 2007.
14. Francesco Camastra, Marco Spinetti, and Alessandro Vinciarelli, "Offline cursive character challenge: a new benchmark for machine learning and pattern recognition algorithms.," Proceedings of the 18th International Conference on Pattern Recognition, pp. 913-916, 2006.
15. C.-C. Chang and C.-J. Lin. LIBSVM: a library for support vector machines. Disponível em: http://www.csie.ntu.edu.tw/~cjlin/libsvm/.Acessado em: 09/2003, 2004.
16. Freitas, C.; Bortolozzi, F.; Sabourin, R. Handwritten isolated word recognition: An approach based on mutual information for feature set validation. Proceedings 6th Internacional Conference on Document Analisys and Recognition, p. 665-669, 2001.
17. Cai, J.; Liu, Z.-Q. Integration of structural and statistical information of unconstrained handwritten numeral recognition. IEE Transactions on Pattern Analysis and Machine Intelligence, v. 21, n. 3, p. 263-270, 1999.
18. Nadal C, Legault R, Suen CY (1990) Complementary Algorithms for the Recognition of Totally Unconstrained Handwritten Numerals. In: Proceedings of the tenth International Conference on Pattern Recognition 1990. Atlantic City, NJ, USA, pp 443-449.
19. M. Riedmiller and H. Braun, "A direct adaptive method for faster backpropagation learning: The rprop algorithm," Proceedings of the IEEE International Conference on Neural Networks, pp. 586-591, 1993.
20. John Thornton, Michael Blumenstein, Vu Nguyen, and Trevor Hine, "Offline cursive character recognition: A state-of-the-art comparison,"14th Conference of the International Graphonomics Society, 2009.
21. Liu, C.-L. et al. Handwritten digit recognition: Benchmarking of state-of-the-art techniques. Pattern Recognition, v. 36, p. 2271-2285, 2003.
22. V. N. Vapnik. Statistical Learning Theory. John Wiley and Sons, New York, USA, 1998.
23. Brito, A. S. et al. Foreground and background information in an HMM-based method for recognition of isolated characters and numeral strings. Internacional Workshop on Frontiers in Handwriting Recognition, p. 371-376, 2004.
24. Ho TK, Hull JJ, Srihari SN (1994) Decision Combination in Multiple Classifier Systems. IEEE Transactions on Pattern Analysis and Machine Intelligence 16(1):66-75.
25. Liu, C.-L; Sako, H.; Fujisawa, H. Performance evaluation of pattern classifiers for handwritten character recognitio. Ineternacional Journal on Document Analysis and Recognition - IJDAR, v. 4, p. 191-204, 2002.
26. A. J. Smola and B. Schölkopf. Learning with Kernels. TheMIT Press, Cambridge,MA, 2002.
27. Rodrigues, R. J.; Kupac, G. V.; Thomé, A. C. G.; "Character Feature Extraction Using Polygonal Projection Sweep (Contour Detection)", IWANN2001, Granade - Spain, June 13 a 15, LINCS, 2001, pp 687-695, 2001.
28. G. Vamvakas; B. Gatos; S. J. Perantonis; "Handwritten character recognition through two-stage foreground sub-sampling"; Pattern Recognition 43 (2010) 2807-2816.
29. Rodrigues, R. J.; Silva, Eugênio; Thomé, A. C. G.; "Feature Extraction Using Contour Projection"; 5th World Multiconference on Systemics, Cybernetics and Informatics SCI2001 - Orlando - USA, July 22-25.
30. Rodrigues, R. J.; Silva, Eugênio; Thomé, A. C. G. "Extração de Características para o Reconhecimento de Letras Manuscritas" (In Portuguese), 2002.
31. Aires, S.B.K., Reconhecimento de caracteres manuscritos baseados em regiões perceptivas (In Portuguese). Dissertação de Mestrado. Pontifícia Universidade Católica do Paraná. Curitiba. 2005.
32. Veloso, L. R., Reconhecimento de Caracteres Numéricos Manuscritos (In Portuguese). Dissertação de Mestrado, Universidade Federal da Paraíba, Campina Grande, 1998.
33. Rafael M. O. Cruz, George D. C. Cavalcanti and Tsang Ing Ren. An Ensemble Classifier For Offline Cursive Character Recognition Using Multiple Feature Extraction Techniques. IEEE International Joint Conference on Neural Networks (IJCNN), Barcelona, p. 744-751, 2010.
34. Haykin, S. Neural Networks - A Comprehensive Foundation. [S.I.]: Prentice Hall, 1996.
35. Oliveira, L. S.; Sabourin, R. Support vector machines for handwritten numerical string recognition. 9th International Workshop on Frontiers in Handwiting Recognition (IWFHR-9), p. 39-44, 2004.
36. Teow, L.-N.; Loe, K.-F. Robust vision-based features and classification schemes for off-line handwritten digit recognition. Pattern Recognition, v. 35, p. 2355-2364, 2002.
37. Vuurpijl, L.; Schomaker, L.; Van, M. Architectures for detecting and solving conflits: Two-stage classification and support vector classifiers. International Journal on Document Analisys and Recognition, p. 213-223, 2003.
38. Oh, I. -S.; Suen, C. Y. A class-modular feedforward neural network for handwriting recognition. Pattern Recognition, V. 35, p. 229-244, 2002.
39. John Thornton, Jolon Faichney, Michael Blumenstein, and Trevor Hine, "Character recognition using hierarchical vector quantization and temporal pooling," Proceedings of the 21st Australasian Joint Conference on Artificial Intelligence, pp. 562-572, 2008.
40. John Thornton, Michael Blumenstein, Vu Nguyen, and Trevor Hine, "Offline cursive character recognition: A state-of-the-art comparison," 14th Conference of the International Graphonomics Society, 2009.
41. Bellili A., Gilloux M., and Gallinari P., "An Hybrid MLP-SVM Handwritten Digit Recognizer," icdar, pp.0028, Sixth International Conference on Document Analysis and Recognition (ICDAR'01), 2001.
42. Duda, Hart, and Stork, "Pattern Classification" Chapter 5, Wiley, 2000.
43. George, D., Hawkins, J. (2005). A hierarchical Bayesian model of invariant pattern recognition in the visual cortex. Proc. Int. Joint Conf. on Neural Networks - IJCNN-05, Montreal, Canada, pp. 1812-1818.
44. Blumenstein, M., Verma, B. & Basli, H. (2003). A novel feature extraction technique for the recognition of segmented handwritten characters. Proc. Int. Conf. On Document Analysis and Recognition -ICDAR-03, pp. 137-141.
45. Gader, P. D., Mohamed, M. & Chiang, J. H. (1997). Handwritten word recognition with character and inter-character neural networks, IEEE Trans. Syst. Man Cybernet.-part B: Cybernetics 27, 158-164.
46. Blumenstein, M., Liu, X. Y. & Verma, B. (2007). An investigation of the modified direction feature vector for cursive character recognition. Pattern Recognition 40(2), 376-388.
47. P.A. Devijver and J. Kittler, Pattern Recognition: A Statistical Approach, Prentice-Hall, Londres, 1982.
48. Oivind Due Trier, Anil K Jain, and Torfinn Taxt, "Feature extraction methods for character recognition: A survey," Pattern Recognition, vol. 29, no. 4, pp. 641-662, 1995.
49. Oivind Due Trier, Anil K Jain, and Torfinn Taxt, "Feature extraction methods for character recognition: A survey," Pattern Recognition, vol. 29, no. 4, pp. 641-662, 1995.
50. Plamondon R., Srihari S. N., "On-Line and Off-Line Handwriting Recognition: A Comprehensive Survey," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.22, p. p. 63-84, 2000.
51. Kecman V., "Learning and Soft Computing, Support Vector machines, Neural Networks and Fuzzy Logic Models", The MIT Press, Cambridge, MA, 2001.
52. Wang L.P. (Ed.), "Support Vector Machines: Theory and Application", Springer, Berlin, 2005.
53. Rumelhard, D.E., Weigend, S.A.; "Predicting the Future: A Connectionist Approach", International Journal of Neural Systems, pp. 193-209, 1990.