Skip to main content
SpringerLink
Log in

Ensemble Methods to Improve the Performance of an English Handwritten Text Line Recognizer

  • Conference paper
Arabic and Chinese Handwriting Recognition (SACH 2006)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 4768))

Included in the following conference series:

Abstract

This paper describes recent work on ensemble methods for offline handwritten text line recognition. We discuss techniques to build ensembles of recognizers by systematically altering the training data or the system architecture. To combine the results of the ensemble members, we propose to apply ROVER, a voting based framework commonly used in continuous speech recognition. Additionally, we extend this framework with a statistical combination method. The experimental evaluation shows that the proposed ensemble methods have the potential to improve the recognition accuracy compared to a single recognizer.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Impedovo, S., Wang, P., Bunke, H. (eds.): Automatic Bankcheck Processing. World Scientific, Singapore (1997)

    MATH  Google Scholar 

  2. Gorski, N., Anisimov, V., Augustin, E., Baret, O., Maximov, S.: Industrial bank check processing: the A2iA CheckReaderTM. International Journal on Document Analysis and Recognition 3(4), 1433–2833 (2001)

    Article  Google Scholar 

  3. Mahadevan, U., Srihari, S.: Parsing and recognition of city, state, and zip codes inhandwritten addresses. In: Proc. 5th International Conference on Document Analysis and Recognition, Bangalore, India, pp. 325–328 (1999)

    Google Scholar 

  4. 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 

  5. 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 Publ. Co., Singapore (1999)

    Google Scholar 

  6. Senior, A., Robinson, A.: An off-line cursive handwriting recognition system. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(3), 309–321 (1998)

    Article  Google Scholar 

  7. 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)

    Article  Google Scholar 

  8. 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)

    Article  Google Scholar 

  9. Dasarathy, B.V.: Decision Fusion. IEEE Computer Society Press, Los Alamitos, USA (1994)

    Google Scholar 

  10. Oza, N., Polikar, R., Kittler, J., Roli, F. (eds.): MCS 2005. LNCS, vol. 3541. Springer, Heidelberg (2005)

    MATH  Google Scholar 

  11. Kuncheva, L.I.: Combining Pattern Classifiers: Methods and Algorithms. John Wiley & Sons Inc., Chichester (2004)

    MATH  Google Scholar 

  12. Sirlantzkis, K., Fairhurst, M., Hoque, M.: 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)

    Chapter  Google Scholar 

  13. 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)

    Article  Google Scholar 

  14. 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)

    Article  Google Scholar 

  15. Ye, X., Cheriet, M., Suen, C.Y.: StrCombo: combination of string recognizers. Pattern Recognition Letters 23, 381–394 (2002)

    Article  MATH  Google Scholar 

  16. Gader, P., Mohamed, M., Keller, J.: Fusion of handwritten word classifiers. Pattern Recognition Letters 17, 577–584 (1996)

    Article  Google Scholar 

  17. Günter, S., Bunke, H.: Ensembles of classifiers for handwritten word recognition. International Journal on Document Analysis and Recognition 5(4), 224–232 (2003)

    Article  Google Scholar 

  18. 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)

    Chapter  Google Scholar 

  19. Bertolami, R., Bunke, H.: Multiple handwritten text recognition systems derived from specific integration of a language model. In: Proc. 8th International Conference on Document Analysis and Recognition, Seoul, Korea, vol. 1, pp. 521–524 (2005)

    Google Scholar 

  20. Bertolami, R., Bunke, H.: Ensemble methods for handwritten text line recognition systems. In: Proc. International Conference on Systems, Man and Cybernetics, Hawaii, USA, pp. 2334–2339 (2005)

    Google Scholar 

  21. Bertolami, R., Bunke, H.: Diversity analysis for ensembles of word sequence recognisers. In: Yeung, D.-Y., et al. (eds.) SSPR 2006 and SPR 2006. LNCS, vol. 4109, pp. 667–686. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  22. Bertolami, R., Bunke, H.: Multiple classifier methods for offline handwritten text line recognition. In: Haindl, M., Kittler, J., Roli, F. (eds.) MCS 2007. LNCS, vol. 4472, pp. 72–81. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  23. 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)

    Article  Google Scholar 

  24. Bertolami, R., Zimmermann, M., Bunke, H.: Rejection strategies for offline handwritten text line recognition. Pattern Recognition Letters 27(16), 2005–2012 (2006)

    Article  Google Scholar 

  25. Dietterich, T.: Ensemble methods in machine learning. In: 1st International Workshop on Multiple Classifier Systems, Cagliari, Italy, pp. 1–15 (2000)

    Google Scholar 

  26. Brown, G., Wyatt, J., Harris, R., Yao, X.: Diversity creation methods: A survey and categorisation. Information Fusion 6, 5–20 (2005)

    Article  Google Scholar 

  27. Windeatt, T.: Diversity measures for multiple classifier system analysis and design. Information Fusion 6(1), 21–36 (2004)

    Article  Google Scholar 

  28. Kohavi, R.: A study of cross-validation and bootstrap for accuracy estimation and model selection. In: Proc. International Joint Conference on Artificial Intelligence, pp. 1137–1145 (1995)

    Google Scholar 

  29. Breiman, L.: Bagging predictors. Machine Learning 24(2), 123–140 (1996)

    MATH  MathSciNet  Google Scholar 

  30. Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. In: Proc. European Conference on Computational Learning Theory, pp. 23–37 (1995)

    Google Scholar 

  31. Partridge, D., Yates, W.B.: Engineering multiversion neural-net systems. Neural Computation 8(4), 869–893 (1996)

    Article  Google Scholar 

  32. Ho, T.K.: The random space method for constructing decision forests. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(8), 832–844 (1998)

    Article  Google Scholar 

  33. Zeppenfeld, T., Finke, M., Ries, K., Westphal, M., Waibel, A.: Recognition of conversational telephone speech using the janus speech engine. In: Proc. International Conference on Acoustics, Speech, and Signal Processing, Munich, Germany, pp. 1815–1818 (1997)

    Google Scholar 

  34. Rahmann, A., Fairhurst, M.: Multiple expert classification: A new methodology for parallel decision fusion. International Journal on Document Analysis and Recognition 3(1), 40–55 (2000)

    Article  Google Scholar 

  35. Ho, T.K., Hull, J.J., Srihari, S.N.: Decision combination in multiple classifier systems. IEEE Transactions on Pattern Analysis and Machine Intelligence 16(1), 66–75 (1994)

    Article  Google Scholar 

  36. Wang, W., Brakensiek, A., Rigoll, G.: Combination of multiple classifiers for handwritten word recognition. In: Proc. 8th International Workshop on Frontiers in Handwriting Recognition, Niagara-on-the-Lake, Canada, pp. 117–122 (2002)

    Google Scholar 

  37. Fiscus, J.: A post-processing system to yield reduced word error rates: Recognizer output voting error reduction. In: Proc. IEEE Workshop on Automatic Speech Recognition and Understanding, Santa Barbara, pp. 347–352 (1997)

    Google Scholar 

  38. Wagner, R., Fischer, M.: The string-to-string correction problem. Journal of the ACM 21(1), 168–173 (1974)

    Article  MATH  MathSciNet  Google Scholar 

  39. 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)

    Article  Google Scholar 

  40. 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)

    Article  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute of Computer Science and Applied Mathematics, University of Bern, Neubrückstrasse 10, CH-3012, Bern, Switzerland

    Roman Bertolami & Horst Bunke

Authors
  1. Roman Bertolami
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Horst Bunke
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

David Doermann Stefan Jaeger

Rights and permissions

Reprints and permissions

Copyright information

© 2008 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Bertolami, R., Bunke, H. (2008). Ensemble Methods to Improve the Performance of an English Handwritten Text Line Recognizer. In: Doermann, D., Jaeger, S. (eds) Arabic and Chinese Handwriting Recognition. SACH 2006. Lecture Notes in Computer Science, vol 4768. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78199-8_16

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-78199-8_16

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-78198-1

  • Online ISBN: 978-3-540-78199-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation