Evaluation of Classical and Novel Ensemble Methods for Handwritten Word Recognition

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

Volumn 3138, Issue , 2004, Pages 583-591

  Günter, Simon ^a   Bunke, Horst ^a  

^a UNIVERSITY OF BERN   (Switzerland)

Author keywords

Ensemble methods; Handwritten word recognition; Hidden markov model (hmm)

Indexed keywords

ARTIFICIAL INTELLIGENCE; HIDDEN MARKOV MODELS; LEARNING SYSTEMS; MARKOV PROCESSES; PATTERN RECOGNITION; VOCABULARY CONTROL;

COMBINATION METHOD; ENSEMBLE METHODS; HAND-WRITTEN TEXT RECOGNITION; HANDWRITING RECOGNITION; HANDWRITTEN WORD RECOGNITION; RECOGNITION SYSTEMS;

CHARACTER RECOGNITION;

EID: 34147123570     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-27868-9_63     Document Type: Article

References (30)

1. Proc of the 7th Int. Conf. on Document Analysis and Recognitio, Edinbourgh, Scotland, 2003.
2. Leo Breiman. Bagging predictors. Machine Learning, (2):123-140, 1996.
3. T. G. Dietterich. Ensemble methods in machine learning. In [17], pages 1-15.
4. 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.
5. Yoav Freund and Robert E. Schapire. A decision-theoretic generalisation of online learning and an application to boosting. Journal of Computer and Systems Sciences, 55(1):119-139, 1997.
6. S. Günter and H. Bunke. Generating classifier ensembles from multiple prototypes and its application to handwriting recognition. In [18], pages 179-188.
7. S. Günter and H. Bunke. New boosting algorithms for classification problems with large number of classes applied to a handwritten word recognition task. In [27], pages 326-335.
8. 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.
9. S. Günter and H. Bunke. Creation of classifier ensembles for handwritten word recognition using feature selection algorithms. In Proc. of the 8th International Conference on Frontiers in Handwriting Recognition, pages 183-188, 2002.
10. S. Günter and H. Bunke. A new combination scheme for HMM-based classifiers and its application to handwriting recognition. In 16th International Conference on Pattern Recognition, volume 2, pages 332-337, Quebec, Canada, 2002.
11. S. Günter and H. Bunke. Ensembles of classifiers for handwritten word recognition. International Journal of Document Analysis and Recognition, 5(4):224-232, 2003.
12. T. K. Ho. The random subspace method for constructing decision forests. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(8):832-844, 1998.
13. S. Impedovo, P. Wang, and H. Bunke, editors. Automatic Bankcheck Processing. World Scientific Publ. Co, Singapore, 1997.
14. A. Jain, M. Murty, and P. Flynn. Data clustering: A survey. ACM Computing Survey, 31:264-323, 1999.
15. 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.
16. 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.
17. J. Kittler and F. Roli, editors. First International Workshop on Multiple Classifier Systems, Cagliari, Italy, 2000. Springer.
18. J. Kittler and F. Roli, editors. Third International Workshop on Multiple Classifier Systems, Cagliari, Italy, 2002. Springer.
19. L. Lam, Y.-S. Huang, and C. Suen. Combination of multiple classifier decisions for optical character recognition. In H. Bunke and P. Wang, editors, Handbook of Character Recognition and Document Image Analysis, pages 79-101. World Scientific, 1997.
20. 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.
21. U. Marti and H. Bunke. The IAM-database: An English sentence database for offline handwriting recognition. Int. Journal of Document Analysis and Recognition, 5:39-46, 2002.
22. 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.
23. D. Partridge and W. B. Yates. Engineering multiversion neural-net systems. Neural Computation, 8(4):869-893, 1996.
24. L. Rabiner. A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE, 77(2):257-285, 1989.
25. J.-C. Simon. Off-line cursive word recognition. Special Issue of Proc. of the IEEE, 80(7):1150-1161, July 1992.
26. 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.
27. T. Windeatt and F. Roli, editors. 4th Int. Workshop on Multiple Classifier Systems, Guildford, United Kingdom, 2003. Springer.
28. 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.
29. M. Zimmermann and H. Bunke. Automatic segmentation of the IAM off-line database for handwritten English text. In Proc. of the 16th Int. Conference on Pattern Recognition, volume 4, pages 35-39, Quebec, Canada, 2002.
30. 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.