article

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

Authors:

Roman Bertolami,

Horst BunkeAuthors Info & Claims

Pattern Recognition, Volume 41, Issue 11

Pages 3452 - 3460

https://doi.org/10.1016/j.patcog.2008.04.003

Published: 01 November 2008 Publication History

Abstract

This paper investigates various ensemble methods for offline handwritten text line recognition. To obtain ensembles of recognisers, we implement bagging, random feature subspace, and language model variation methods. For the combination, the word sequences returned by the individual ensemble members are first aligned. Then a confidence-based voting strategy determines the final word sequence. A number of confidence measures based on normalised likelihoods and alternative candidates are evaluated. Experiments show that the proposed ensemble methods can improve the recognition accuracy over an optimised single reference recogniser.

References

[1]

Impedovo, S., Ottaviano, L. and Occhiegro, S., Optical character recognition-a survey. Int. J. Pattern Recognition Artif. Intell. v5. 1-24.

[2]

Suen, C., Nadal, C., Legault, R., Mai, T. and Lam, L., Computer recognition of unconstrained handwritten numerals. Proc. IEEE. v80 i7. 1162-1180.

[3]

In: Impedovo, S., Wang, P., Bunke, H. (Eds.), Automatic Bankcheck Processing, World Scientific, Singapore.

[4]

Brakensiek, A. and Rigoll, G., Handwritten address recognition using hidden Markov models. In: Dengel, A., Junker, M., Weisbecker, A. (Eds.), Reading and Learning, Springer, Berlin. pp. 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, World Scientific, Singapore. pp. 163-172.

[6]

Senior, A. and Robinson, A., An off-line cursive handwriting recognition system. IEEE Trans. Pattern Anal. Mach. Intell. v20 i3. 309-321.

Digital Library

[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. v26 i6. 709-720.

Digital Library

[8]

Zimmermann, M., Chappelier, J.-C. and Bunke, H., Offline grammar-based recognition of handwritten sentences. IEEE Trans. Pattern Anal. Mach. Intell. v28 i5. 818-821.

Digital Library

[9]

Dasarathy, B.V., Decision Fusion. 1994. IEEE Computer Society Press, Los Alamitos, CA, USA.

Digital Library

[10]

Kuncheva, L.I., Combining Pattern Classifiers: Methods and Algorithms. 2004. Wiley, NY.

Digital Library

[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.

Digital Library

[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. v17 i1. 90-94.

Digital Library

[13]

Oliveira, L.S., Morita, M. and Sabourin, R., Feature selection for ensembles applied to handwriting recognition. Int. J. Document Anal. Recognition. v8 i4. 262-279.

[14]

Ye, X., Cheriet, M. and Suen, C.Y., StrCombo: combination of string recognizers. Pattern Recognition Lett. v23. 381-394.

Digital Library

[15]

Gader, P., Mohamed, M. and Keller, J., Fusion of handwritten word classifiers. Pattern Recognition Lett. v17. 577-584.

Digital Library

[16]

Günter, S. and Bunke, H., Ensembles of classifiers for handwritten word recognition. Int. J. Document Anal. Recognition. v5 i4. 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.

Digital Library

[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.

Digital Library

[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.

Digital Library

[20]

Fiscus, J., A post-processing system to yield reduced word error rates: recognizer output voting error reduction. . In: Proceedings of the IEEE Workshop on Automatic Speech Recognition and Understanding, pp. 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. v15. 65-90.

[22]

Rabiner, L., A tutorial on hidden Markov models and selected application in speech recognition. Proc. IEEE. v77 i2. 257-286.

Digital Library

[23]

Pitrelli, J.F., Subrahmonia, J. and Perrone, M.P., Confidence modeling for handwriting recognition: algorithms and applications. Int. J. Document Anal. Recognition. v8 i1. 35-46.

Digital Library

[24]

Fink, G., Markov Models for Pattern Recognition, From Theory to Applications. 2007. Springer, Heidelberg.

Digital Library

[25]

Toselli, A.H., Romero, V., Vidal, E. and Rodriguez, L., Computer assisted transcription of handwritten text images. In: Proceedings of the 9th International Conference on Document Analysis and Recognition, pp. 944-948.

Digital Library

[26]

Viterbi, A., Error bounds for convolutional codes and an asymptotically optimal decoding algorithm. IEEE Trans. Inform. Theory. v13 i2. 260-269.

Digital Library

[27]

Kneser, R. and Ney, H., Improved backing-off for m-gram language modeling. In: Proceedings of the International Conference on Acoustics, Speech, and Signal Processing, pp. 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.

Digital Library

[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.

Digital Library

[30]

Koerich, A.L., Rejection strategies for handwritten word recognition. In: Proceedings of the 9th International Workshop on Frontiers in Handwriting Recognition, pp. 479-484.

Digital Library

[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.

Digital Library

[32]

Zeppenfeld, T., Finke, M., Ries, K., Westphal, M. and Waibel, A., Recognition of conversational telephone speech using the JANUS speech engine. In: Proceedings of the International Conference on Acoustics, Speech, and Signal Processing, pp. 1815-1818.

Digital Library

[33]

Bertolami, R., Zimmermann, M. and Bunke, H., Rejection strategies for offline handwritten text line recognition. Pattern Recognition Lett. v27 i16. 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.

Digital Library

[35]

Wagner, R. and Fischer, M., The string-to-string correction problem. J. ACM. v21 i1. 168-173.

Digital Library

[36]

Brown, G., Wyatt, J., Harris, R. and Yao, X., Diversity creation methods: a survey and categorisation. Inform. Fusion. v6. 5-20.

[37]

Windeatt, T., Diversity measures for multiple classifier system analysis and design. Inform. Fusion. v6 i1. 21-36.

[38]

Kohavi, R., A study of cross-validation and bootstrap for accuracy estimation and model selection. In: Proceedings of the International Joint Conference on Artificial Intelligence, pp. 1137-1145.

Digital Library

[39]

Breiman, L., Bagging predictors. Mach. Learn. v24 i2. 123-140.

Digital Library

[40]

Freund, Y. and Schapire, R.E., A decision-theoretic generalization of on-line learning and an application to Boosting. In: Proceedings of the European Conference on Computational Learning Theory, pp. 23-37.

Digital Library

[41]

Ho, T.K., The random space method for constructing decision forests. IEEE Trans. Pattern Anal. Mach. Intell. v20 i8. 832-844.

Digital Library

[42]

Partridge, D. and Yates, W.B., Engineering multiversion neural-net systems. Neural Comput. v8 i4. 869-893.

Digital Library

[43]

Bertolami, R. and Bunke, H., Ensemble methods for handwritten text line recognition systems. In: Proceedings of the International Conference on Systems, Man and Cybernetics, pp. 2334-2339.

[44]

Rahmann, A. and Fairhurst, M., Multiple expert classification: a new methodology for parallel decision fusion. Int. J. Document Anal. Recognition. v3 i1. 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. v16 i1. 66-75.

Digital Library

[46]

Wang, W., Brakensiek, A. and Rigoll, G., Combination of multiple classifiers for handwritten word recognition. In: Proceedings of the 8th International Workshop on Frontiers in Handwriting Recognition, pp. 117-122.

Digital Library

[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. v5. 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.

Digital Library

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Published In

cover image Pattern Recognition

Pattern Recognition Volume 41, Issue 11

November, 2008

249 pages

ISSN:0031-3203

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Copyright © Elsevier Ltd © 2008.

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Elsevier Science Inc.

United States

Publication History

Published: 01 November 2008

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https://dl.acm.org/doi/10.1109/TPAMI.2024.3367245
El Bahi HZatni A(2022)Text recognition in document images obtained by a smartphone based on deep convolutional and recurrent neural networkMultimedia Tools and Applications10.1007/s11042-019-07855-z78:18(26453-26481)Online publication date: 10-Mar-2022
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Ott FRügamer DHeublein LHamann TBarth JBischl BMutschler C(2022)Benchmarking online sequence-to-sequence and character-based handwriting recognition from IMU-enhanced pensInternational Journal on Document Analysis and Recognition10.1007/s10032-022-00415-625:4(385-414)Online publication date: 22-Sep-2022
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