Hidden Markov model-based ensemble methods for offline handwritten text line recognition

Pattern Recognition

Volumn 41, Issue 11, 2008, Pages 3452-3460

  Bertolami, Roman ^a   Bunke, Horst ^a  

^a UNIVERSITY OF BERN   (Switzerland)

Author keywords

Confidence measures; Ensemble methods; Offline handwritten text line recognition

Indexed keywords

COMPUTATIONAL LINGUISTICS; HIDDEN MARKOV MODELS; LEARNING SYSTEMS; MARKOV PROCESSES;

CONFIDENCE MEASURES; ELSEVIER (CO); ENSEMB LE METHODS; ENSEMBLE MEMBERS; FEATURE SUBSPACE; HANDWRITTEN TEXT LINE RECOGNITION; INDIVIDUAL (PSS 544-7); LANGUAGE MODELLING; MARKOV MODELLING; RECOGNITION ACCURACY; SINGLE REFERENCE (SR); VOTING STRATEGIES; WORD SEQUENCES;

INFORMATION RETRIEVAL SYSTEMS;

EID: 48149107876     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patcog.2008.04.003     Document Type: Article

References (52)

1. Impedovo S., Ottaviano L., and Occhiegro S. Optical character recognition-a survey. Int. J. Pattern Recognition Artif. Intell. 5 (1991) 1-24
2. Suen C., Nadal C., Legault R., Mai T., and Lam L. Computer recognition of unconstrained handwritten numerals. Proc. IEEE 80 7 (1992) 1162-1180
3. In: Impedovo S., Wang P., and Bunke H. (Eds). Automatic Bankcheck Processing (1997), World Scientific, Singapore
4. Brakensiek A., and Rigoll G. Handwritten address recognition using hidden Markov models. In: Dengel A., Junker M., and Weisbecker A. (Eds). Reading and Learning (2004), Springer, Berlin 103-122
5. Kim G., Govindaraju V., and Srihari S. An architecture for handwritten text recognition systems. In: Lee S.-W. (Ed). Advances in Handwriting Recognition (1999), World Scientific, Singapore 163-172
6. Senior A., and Robinson A. An off-line cursive handwriting recognition system. IEEE Trans. Pattern Anal. Mach. Intell. 20 3 (1998) 309-321
7. Vinciarelli A., Bengio S., and Bunke H. Offline recognition of unconstrained handwritten texts using HMMs and statistical language models. IEEE Trans. Pattern Anal. Mach. Intell. 26 6 (2004) 709-720
8. Zimmermann M., Chappelier J.-C., and Bunke H. Offline grammar-based recognition of handwritten sentences. IEEE Trans. Pattern Anal. Mach. Intell. 28 5 (2006) 818-821
9. Dasarathy B.V. Decision Fusion (1994), IEEE Computer Society Press, Los Alamitos, CA, USA
10. Kuncheva L.I. Combining Pattern Classifiers: Methods and Algorithms (2004), Wiley, NY
11. K. Sirlantzkis, M. Fairhurst, M. Hoque, Genetic algorithms for multi-classifier system configuration: a case study in character recognition, in: 2nd International Workshop on Multiple Classifier Systems, Cambridge, England, Lecture Notes in Computer Science, vol. 2096, Springer, Berlin, 2001, pp. 99-108.
12. Huang Y., and Suen C. A method of combining multiple experts for the recognition of unconstrained handwritten numerals. IEEE Trans. Pattern Anal. Mach. Intell. 17 1 (1995) 90-94
13. Oliveira L.S., Morita M., and Sabourin R. Feature selection for ensembles applied to handwriting recognition. Int. J. Document Anal. Recognition 8 4 (2006) 262-279
14. Ye X., Cheriet M., and Suen C.Y. StrCombo: combination of string recognizers. Pattern Recognition Lett. 23 (2002) 381-394
15. Gader P., Mohamed M., and Keller J. Fusion of handwritten word classifiers. Pattern Recognition Lett. 17 (1996) 577-584
16. Günter S., and Bunke H. Ensembles of classifiers for handwritten word recognition. Int. J. Document Anal. Recognition 5 4 (2003) 224-232
17. U.-V. Marti, H. Bunke, Use of positional information in sequence alignment for multiple classifier combination, in: J. Kittler, F. Roli (Eds.), 2nd International Workshop on Multiple Classifier Systems, Cambridge, England, Lecture Notes in Computer Science, vol. 2096, Springer, Berlin, 2001, pp. 388-398.
18. R. Bertolami, H. Bunke, Multiple handwritten text recognition systems derived from specific integration of a language model, in: Proceedings of the 8th International Conference on Document Analysis and Recognition, Seoul, Korea, vol. 1, 2005, pp. 521-524.
19. R. Bertolami, H. Bunke, Multiple classifier methods for offline handwritten text line recognition, in: M. Haindl, J. Kittler, F. Roli (Eds.), 7th International Workshop on Multiple Classifier Systems, Prague, Czech Republic, Lecture Notes in Computer Science, vol. 4472, Springer, Berlin, 2007, pp. 72-81.
20. Fiscus J. A post-processing system to yield reduced word error rates. recognizer output voting error reduction. Proceedings of the IEEE Workshop on Automatic Speech Recognition and Understanding. Santa Barbara (1997) 347-352
21. Marti U.-V., and Bunke H. Using a statistical language model to improve the performance of an HMM-based cursive handwriting recognition system. Int. J. Pattern Recognition Artif. Intell. 15 (2001) 65-90
22. Rabiner L. A tutorial on hidden Markov models and selected application in speech recognition. Proc. IEEE 77 2 (1989) 257-286
23. Pitrelli J.F., Subrahmonia J., and Perrone M.P. Confidence modeling for handwriting recognition: algorithms and applications. Int. J. Document Anal. Recognition 8 1 (2006) 35-46
24. Fink G. Markov Models for Pattern Recognition, From Theory to Applications (2007), Springer, Heidelberg
25. Toselli A.H., Romero V., Vidal E., and Rodriguez L. Computer assisted transcription of handwritten text images. Proceedings of the 9th International Conference on Document Analysis and Recognition. Curitiba, Brazil (2007) 944-948
26. Viterbi A. Error bounds for convolutional codes and an asymptotically optimal decoding algorithm. IEEE Trans. Inform. Theory 13 2 (1967) 260-269
27. Kneser R., and Ney H. Improved backing-off for m-gram language modeling. Proceedings of the International Conference on Acoustics, Speech, and Signal Processing. Detroit, USA (1995) 181-184
28. N. Gorski, Optimizing error-reject trade off in recognition systems, in: Proceedings of the 4th International Conference on Document Analysis and Recognition, vol. 2, Ulm, Germany, 1997, pp. 1092-1096.
29. J. Pitrelli, M.P. Perrone, Confidence-scoring post-processing for off-line handwritten-character recognition verification, in: Proceedings of the 7th International Conference on Document Analysis and Recognition, vol. 1, Edinburgh, Scotland, 2003, pp. 278-282.
30. Koerich A.L. Rejection strategies for handwritten word recognition. Proceedings of the 9th International Workshop on Frontiers in Handwriting Recognition. Tokyo, Japan (2004) 479-484
31. A. Sanchis, V. Jimenez, E. Vidal, Efficient use of the grammar scale factor to classify incorrect words in speech recognition verification, in: Proceedings of the International Conference on Pattern Recognition, vol. 3, Barcelona, Spain, 2000, pp. 278-281.
32. Zeppenfeld T., Finke M., Ries K., Westphal M., and Waibel A. Recognition of conversational telephone speech using the JANUS speech engine. Proceedings of the International Conference on Acoustics, Speech, and Signal Processing. Munich, Germany (1997) 1815-1818
33. Bertolami R., Zimmermann M., and Bunke H. Rejection strategies for offline handwritten text line recognition. Pattern Recognition Lett. 27 16 (2006) 2005-2012
34. M. Zimmermann, R. Bertolami, H. Bunke, Rejection strategies for offline handwritten sentence recognition, in: Proceedings of the 17th International Conference on Pattern Recognition, vol. 2, Cambridge, England, 2004, pp. 550-553.
35. Wagner R., and Fischer M. The string-to-string correction problem. J. ACM 21 1 (1974) 168-173
36. Brown G., Wyatt J., Harris R., and Yao X. Diversity creation methods: a survey and categorisation. Inform. Fusion 6 (2005) 5-20
37. Windeatt T. Diversity measures for multiple classifier system analysis and design. Inform. Fusion 6 1 (2004) 21-36
38. Kohavi R. A study of cross-validation and bootstrap for accuracy estimation and model selection. Proceedings of the International Joint Conference on Artificial Intelligence (1995) 1137-1145
39. Breiman L. Bagging predictors. Mach. Learn. 24 2 (1996) 123-140
40. Freund Y., and Schapire R.E. A decision-theoretic generalization of on-line learning and an application to Boosting. Proceedings of the European Conference on Computational Learning Theory (1995) 23-37
41. Ho T.K. The random space method for constructing decision forests. IEEE Trans. Pattern Anal. Mach. Intell. 20 8 (1998) 832-844
42. Partridge D., and Yates W.B. Engineering multiversion neural-net systems. Neural Comput. 8 4 (1996) 869-893
43. Bertolami R., and Bunke H. Ensemble methods for handwritten text line recognition systems. Proceedings of the International Conference on Systems, Man and Cybernetics. Hawaii, USA (2005) 2334-2339
44. Rahmann A., and Fairhurst M. Multiple expert classification: a new methodology for parallel decision fusion. Int. J. Document Anal. Recognition 3 1 (2000) 40-55
45. Ho T.K., Hull J.J., and Srihari S.N. Decision combination in multiple classifier systems. IEEE Trans. Pattern Anal. Mach. Intell. 16 1 (1994) 66-75
46. Wang W., Brakensiek A., and Rigoll G. Combination of multiple classifiers for handwritten word recognition. Proceedings of the 8th International Workshop on Frontiers in Handwriting Recognition. Niagara-on-the-Lake, Canada (2002) 117-122
47. Stolcke A., Bratt H., Butzberger J., Franco H., Gadde V., Plauche M., Richey C., Shriberg E., Sonmez K., Zheng J., and Weng F. The SRI March 2000 Hub-5 Conversational Speech Transcription System (2000)
48. Marti U.-V., and Bunke H. The IAM-database: an English sentence database for offline handwriting recognition. Int. J. Document Anal. Recognition 5 (2002) 39-46
49. S. Johansson, E. Atwell, R. Garside, G. Leech, The Tagged LOB Corpus, User's Manual, Norwegian Computing Center for the Humanities, Bergen, Norway, 1986.
50. W.N. Francis, H. Kucera, Brown Corpus Manual. Manual of Information to Accompany a Standard Corpus of Present-Day Edited American English, for use with Digital Computers, Department of Linguistics, Brown University, Providence, RI, USA, 1979.
51. Bauer L. Manual of Information to Accompany the Wellington Corpus of Written New Zealand English (1993), Department of Linguistics, Victoria University, Wellington, New Zealand
52. J. Goodman, A bit of progress in language modeling, Tech. Rep. MSR-TR-2001-72, Microsoft Research, 2001.