Multiple Classifier Methods for Offline Handwritten Text Line Recognition

  • Roman Bertolami
  • Horst Bunke
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4472)

Abstract

This paper investigates the use of multiple classifier methods for offline handwritten text line recognition. To obtain ensembles of recognisers we implement a random feature subspace method. The word sequences returned by the individual ensemble members are first aligned. Then the final word sequence is produced. For this purpose we use a voting method and two novel statistical combination methods. The conducted experiments show that the proposed multiple classifier methods have the potential to improve the recognition accuracy of single recognisers.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Kim, G., Govindaraju, V., Srihari, S.: Architecture for handwritten text recognition systems. In: Lee, S.W. (ed.) Advances in Handwriting Recognition, pp. 163–172. World Scientific, Singapore (1999)Google Scholar
  2. 2.
    Senior, A., Robinson, A.: An off-line cursive handwriting recognition system. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(3), 309–321 (1998)CrossRefGoogle Scholar
  3. 3.
    Vinciarelli, A., Bengio, S., Bunke, H.: Offline recognition of unconstrained handwritten texts using HMMs and statistical language models. IEEE Transactions on Pattern Analysis and Machine Intelligence 26(6), 709–720 (2004)CrossRefGoogle Scholar
  4. 4.
    Zimmermann, M., Chappelier, J.C., Bunke, H.: Offline grammar-based recognition of handwritten sentences. IEEE Transactions on Pattern Analysis and Machine Intelligence 28(5), 818–821 (2006)CrossRefGoogle Scholar
  5. 5.
    Rahman, A., Fairhurst, M.: Multiple classifier decision combination strategies for character recognition: A review. International Journal on Document Analysis and Recognition 5(4), 166–194 (2003)CrossRefGoogle Scholar
  6. 6.
    Kuncheva, L.I.: Combining Pattern Classifiers: Methods and Algorithms. John Wiley & Sons, Chichester (2004)MATHGoogle Scholar
  7. 7.
    Oza, N.C., et al. (eds.): MCS 2005. LNCS, vol. 3541. Springer, Heidelberg (2005)Google Scholar
  8. 8.
    Huang, Y.S., Suen, C.: 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 (1995)CrossRefGoogle Scholar
  9. 9.
    Oliveira, L.S., Morita, M., Sabourin, R.: Feature selection for ensembles applied to handwriting recognition. International Journal on Document Analysis and Recognition 8(4), 262–279 (2006)CrossRefGoogle Scholar
  10. 10.
    Sirlantzis, K., Hoque, M.S., Fairhurst, M.C.: Genetic Algorithms for Multi-classifier System Configuration: A Case Study in Character Recognition. In: Kittler, J., Roli, F. (eds.) MCS 2001. LNCS, vol. 2096, pp. 99–108. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  11. 11.
    Gader, P., Mohamed, M., Keller, J.: Fusion of handwritten word classifiers. Pattern Recognition Letters 17, 577–584 (1996)CrossRefGoogle Scholar
  12. 12.
    Günter, S., Bunke, H.: Multiple classifier systems in off-line handwritten word recognition - on the influence of training set and vocabulary size. International Journal of Pattern Recognition and Artificial Intelligence 18(7), 1303–1320 (2004)CrossRefGoogle Scholar
  13. 13.
    Marti, U.V., Bunke, H.: Use of positional information in sequence alignment for multiple classifier combination. In: Kittler, J., Roli, F. (eds.) MCS 2001. LNCS, vol. 2096, pp. 388–398. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  14. 14.
    Bertolami, R., Bunke, H.: Ensemble methods for handwritten text line recognition systems. In: International Conference on Systems, Man and Cybernetics, Hawaii, USA, pp. 2334–2339 (2005)Google Scholar
  15. 15.
    Fiscus, J.: A post-processing system to yield reduced word error rates: Recognizer output voting error reduction. In: IEEE Workshop on Automatic Speech Recognition and Understanding, Santa Barbara, pp. 347–352 (1997)Google Scholar
  16. 16.
    Marti, U.V., Bunke, H.: Using a statistical language model to improve the performance of an HMM-based cursive handwriting recognition system. International Journal of Pattern Recognition and Artificial Intelligence 15, 65–90 (2001)CrossRefGoogle Scholar
  17. 17.
    Brakensiek, A., Rigoll, G.: Handwritten address recognition using hidden Markov models. In: Dengel, A., Junker, M., Weisbecker, A. (eds.) Reading and Learning, pp. 103–122. Springer, Heidelberg (2004)Google Scholar
  18. 18.
    Ho, T.K.: The random space method for constructing decision forests. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(8), 832–844 (1998)CrossRefGoogle Scholar
  19. 19.
    Xu, L., Krzyzak, A., Suen, C.Y.: Methods of combining multiple classifiers and their applications to handwriting recognition. IEEE Transactions on Systems, Man, and Cybernetics 22(3), 418–435 (1992)CrossRefGoogle Scholar
  20. 20.
    Raudys, S.: Trainable fusion rules. I. Large sample size case. Neural Networks 19(10), 1506–1516 (2006)CrossRefMATHGoogle Scholar
  21. 21.
    Marti, U.V., Bunke, H.: The IAM-database: an English sentence database for offline handwriting recognition. International Journal on Document Analysis and Recognition 5, 39–46 (2002)CrossRefMATHGoogle Scholar
  22. 22.
    Johansson, S., et al.: The Tagged LOB Corpus, User’s Manual. Norwegian Computing Center for the Humanities, Bergen, Norway (1986)Google Scholar
  23. 23.
    Francis, W.N., Kucera, H.: 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, USA (1979)Google Scholar
  24. 24.
    Bauer, L.: Manual of Information to Accompany The Wellington Corpus of Written New Zealand English. Department of Linguistics, Victoria University, Wellington, New Zealand (1993)Google Scholar
  25. 25.
    Goodman, J.: A bit of progress in language modeling. Technical Report MSR-TR-2001-72, Microsoft Research (2001)Google Scholar

Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Roman Bertolami
    • 1
  • Horst Bunke
    • 1
  1. 1.Institute of Computer Science and Applied Mathematics, University of Bern, Neubrückstrasse 10, CH-3012 BernSwitzerland

Personalised recommendations