Skip to main content

Cursive script recognition with time delay neural networks using learning hints

  • Part VI: Speech, Vision, and Pattern Recognition
  • Conference paper
  • First Online:
Artificial Neural Networks — ICANN'97 (ICANN 1997)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1327))

Included in the following conference series:

Abstract

In this paper, we present a system for cursive script recognition. It is built upon several procedures for preprocessing, a neural network to recognize individual characters in a word, and a hidden Markov model for the recognition of complete words. As neural network we use a time delay neural network that receives input from a sliding window, which scans an input word from left to right. The network is trained with the back-propagation algorithm which is expanded by position invariant learning. Because of the enormous need of computation resources we developed a method to include more information in the learning procedure. As a result only about a third of the cycles in the training are needed to achieve the same recognition results. Alternatively keeping the number of training cycles constant, an increase in word recognition rate of about 5% to a total of 87% can be achieved.

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

Access this chapter

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Y. S. Abu-Mostafa. Learning from Hints. Journal of Complexity, Vol. 10, No 1, pp. 165–178, 1994.

    Google Scholar 

  2. U. Bodenhausen, and S. Manke. Application oriented automatic structuring of time-delay neural networks for high performance character and speech recognition. ICANN-93, Proc. of the Int. Conf. on Artificial Neural Networks, Amsterdam, pp. 956–961, Springer Verlag, 1993.

    Google Scholar 

  3. T. M. Breuel. Recognition of Handwritten Responses on US Census Forms. In A. L. Spitz, A. Dengel, editors, Document Analysis Systems, World Scientific, pp. 237–264, 1995.

    Google Scholar 

  4. H. Bunke, M. Roth, and E.G. Schukat-Talamazzini. Off-line cursive handwriting recognition using hidden Markov models. In Pattern Recognition, Vol. 28, No. 9, pp. 1399–1413, 1995.

    Google Scholar 

  5. J.P. Dodel and R. Shinghal. Symbolic/neural recognition of cursive amounts on bank cheques. Proc. ICDAR'95, Montreal 1995. pp 15–18.

    Google Scholar 

  6. G. Kaufmann. H. Bunke and T.M. Ha. Recognition of cursively handwritten words using a combined normalization/perturbation approach. IWFHR 5, Sept. 2–5, 1996, University of Essex, England, pp 17–22.

    Google Scholar 

  7. J. D. Keeler, D.E. Rumelhart, and W.K. Leow. Integrated segmentation and recognition of hand-printed numerals. In David S. Touretzky Richard P. Lippmann, John E. Moody, editor, Neural Information Processing Systems 3, volume 3, pages 557–563. Morgan Kaufmann Publishers, 1989.

    Google Scholar 

  8. J. D. Keeler, D.E. Rumelhart, and W.K. Leow. Integrated segmentation and recognition of hand-printed numerals. MCC Technical Report ACT-NN-010-91, January 1991.

    Google Scholar 

  9. L.R. Rabiner. A tutorial on hidden markov models and selected applications in speech recognition. In Proceedings of the IEEE, volume 77, pages 257–286, February 1989.

    Google Scholar 

  10. H. Weissman, M. Schenkel, I. Guyon, C. Nohl. and D. Henderson. Recognition-Based Segmentation of On-line Run-on Handprinted Words: Input vs. Output Segmentation. Pattern Recognition. volume 27, number 3, pages 405–420, Pergamon Press, Oxford, 1994.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Wulfram Gerstner Alain Germond Martin Hasler Jean-Daniel Nicoud

Rights and permissions

Reprints and permissions

Copyright information

© 1997 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Marti, UV., Kaufmann, G., Bunke, H. (1997). Cursive script recognition with time delay neural networks using learning hints. In: Gerstner, W., Germond, A., Hasler, M., Nicoud, JD. (eds) Artificial Neural Networks — ICANN'97. ICANN 1997. Lecture Notes in Computer Science, vol 1327. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0020279

Download citation

  • DOI: https://doi.org/10.1007/BFb0020279

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-63631-1

  • Online ISBN: 978-3-540-69620-9

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics