Ensembles of classifiers derived from multiple prototypes and their application to handwriting recognition

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 3077, Issue , 2004, Pages 314-323

  Günter, Simon ^a   Bunke, Horst ^a  

^a UNIVERSITY OF BERN   (Switzerland)

Author keywords

Ensemble Method; Handwriting Recognition; Hidden Markov Model (HMM); Multiple Classifier System

Indexed keywords

ALGORITHMS; HIDDEN MARKOV MODELS; MARKOV PROCESSES;

CLASSIFIER ENSEMBLE METHODS; ENSEMBLE METHODS; ENSEMBLES OF CLASSIFIERS; HANDWRITING RECOGNITION; HANDWRITTEN WORDS; MULTIPLE CLASSIFIER SYSTEMS; NOVEL ALGORITHM; PROTOTYPE CLASSIFIERS;

CHARACTER RECOGNITION;

EID: 35048867839     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-25966-4_31     Document Type: Article

References (31)

1. Proc of the 7th Int. Conf. on Document Analysis and Recognition, Edinburgh, Scotland, 2003.
2. A. Brakensiek, J. Rottland, A. Kosmala, and G. Rigoll. Off-line handwriting recognition using various hybrid modeling techniques and character n-grams. In 7th International Workshop on Frontiers in Handwritten Recognition, pages 343-352, 2000.
3. Leo Breiman. Bagging predictors. Machine Learning, (2):123-140, 1996.
4. T. G. Dietterich. Ensemble methods in machine learning. In [17], pages 1-15.
5. T. G. Dietterich and G. Bakiri. Solving multiclass learning problems via error-correcting output codes. Journal of Artifical Intelligence Research, 2:263-286, 1995.
6. T.G. Dietterich and E.B. Kong. Machine learning bias, statistical bias, and statistical variance of decision tree algorithms. Technical report, Departement of Computer Science, Oregon State University, 1995.
7. Yoav Freund and Robert E. Schapire. A decision-theoretic generalisation of on-line learning and an application to boosting. Journal of Computer and Systems Sciences, 55(1):119-139, 1997.
8. S. Günter and H. Bunke. Generating classifier ensembles from multiple prototypes and its application to handwriting recognition. In [18], pages 179-188.
9. S. Günter and H. Bunke. New boosting algorithms for classification problems with large number of classes applied to a handwritten word recognition task. In [28], pages 326-335.
10. S. Günter and H. Bunke. Optimizing the number of states, training iterations and Gaussians in an HMM-based handwritten word recognizer. In [1], volume 1, pages 472-476.
11. T. K. Ho. The random subspace method for constructing decision forests. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(8):832-844, 1998.
12. D. Hull. Using statistical testing in the evaluation of retrieval experiments. In Research and Development in Information Retrieval, pages 329-338. 1993.
13. S. Impedovo, P. Wang, and H. Bunke, editors. Automatic Bankcheck Processing. World Scientific Publ. Co, Singapore, 1997.
14. A. Kaltenmeier, T. Caesar, J.M. Gloger, and E. Mandler. Sophisticated topology of hidden Markov models for cursive script recognition. In Proc. of the 2nd Int. Conf. on Document Analysis and Recognition, Tsukuba Science City, Japan, pages 139-142, 1993.
15. G. Kim, V. Govindaraju, and S.N. Srihari. Architecture for handwritten text recognition systems. In S.-W. Lee, editor, Advances in Handwriting Recognition, pages 163-172. World Scientific Publ. Co., 1999.
16. M. Kirby. Geometric Data Analysis: An Empirical Approach to Dimensionality Reduction and the Study of Patterns. John Wiley and Sons, New York, 2001.
17. J. Kittler and F. Roli, editors. First International Workshop on Multiple Classifier Systems, Cagliari, Italy, 2000. Springer.
18. J. Kittler and P. Roli, editors. Third International Workshop on Multiple Classifier Systems, Cagliari, Italy, 2002. Springer.
19. D. Lee and S. Srihari. Handprinted digit recognition: A comparison of algorithms. In Third International Workshop on Frontiers in Handwriting Recognition, pages 153-162, 1993.
20. U. Marti and H. Bunke. The IAM-database: An English sentence database for off-line handwriting recognition. Int. Journal of Document Analysis and Recognition, 5:39-46, 2002.
21. U.-V. Marti and H. Bunke. Using a statistical language model to improve the performance of an HMM-based cursive handwriting recognition system. Int. Journal of Pattern Recognition and Art. Intelligence, 15:65-90, 2001.
22. D. Partridge and W. B. Yates. Engineering multiversion neural-net systems. Neural Computation, 8(4):869-893, 1996.
23. L. Rabiner. A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE, 77(2):257-285, 1989.
24. J.-C. Simon. Off-line cursive word recognition. Special Issue of Proc. of the IEEE, 80(7):1150-1161, July 1992.
25. C.Y. Suen, C. Nadal, R. Legault, T.A. Mai, and L. Lam. Computer recognition of unconstrained handwritten numerals. Special Issue of Proc. of the IEEE, 80(7):1162-1180, 1992.
26. A. Vinciarelli. Offline Cursive Handwriting: From Word to Text Recognition. PhD thesis, University of Bern, Switzerland, 2003.
27. A. Vinciarelli, S. Bengio, and H. Bunke. Offline recognition of large vocabulary cursive handwritten text. In [1], volume 2, pages 1101-1105.
28. T. Windeatt and F. Roli, editors. 4th Int. Workshop on Multiple Classifier Systems, Guildford, United Kingdom, 2003. Springer.
29. L. Xu, A. Krzyzak, and C. Suen. Methods of combining multiple classifiers and their applications to handwriting recognition. IEEE Transactions on Systems, Man and Cybernetics, 22(3):418-435, 1992.
30. S. J. Young, J. Jansen, J. J. Odell, D. Ollason, and P. C. Woodland. The HTK Hidden Markov Model Toolkit Book. Entropic Cambridge Research Laboratory, http://htk.eng.cam.ac.uk/, 1995.
31. M. Zimmermann and H. Bunke. Hidden Markov model length optimization for handwriting recognition systems. In Proc. of the 8th International Workshop on Frontiers in Handwriting Recognition, pages 369-374, 2002.