Nothing Special   »   [go: up one dir, main page]

Skip to main content

Cursive-Character Script Recognition Using Toeplitz Model and Neural Networks

  • Conference paper
Artificial Intelligence and Soft Computing - ICAISC 2004 (ICAISC 2004)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3070))

Included in the following conference series:

  • 1766 Accesses

Abstract

This paper presents a hybrid method to use both the idea of projection and Toeplitz Matrix approaches to describe the feature vectors of an image and hence identifying it. The method applies two different tools. The main one is Toeplitz forms and the second is Neural Networks. The image model considered in this work are some selected Arabic scripts. The letter is first projected on 12 axes, then the lengths of these axes are measured and afterwards for the sake of classification and recognition these lengths are compared with the ones in the data base. The method has proved its high efficiency upon the other known approaches. Toeplitz model has shown its successful role in improving the description of the image feature vectors and hence increasing the rate of recognition. The overall algorithm has reached a very low rate of misclassification. Both machine and hand written cases have been studied. In this paper, examples of handwritten scripts are considered.

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

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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.

Similar content being viewed by others

References

  1. Saeed, K.: A Projection Approach for Arabic Handwritten Characters Recognition. In: Sincak, P., Vascak, J. (eds.) Quo Vadis Computational Intelligence? New Trends and App. in Comp. Intelligence, pp. 106–111. Physica-Verlag, Berlin (2000)

    Google Scholar 

  2. Burr, D.: “Experiments on Neural Net Recognition of Spoken and Written Text. IEEE Trans. On Acoustic, Speech and Signal Proc. 36(7), 1162–1168 (1988)

    Article  MATH  Google Scholar 

  3. Zurada, J., Barski, M., Jedruch, W.: Artificial Neural Networks. PWN, Warszawa (1996) (in Polish)

    Google Scholar 

  4. Saeed, K., Rybnik, M., Tabedzki, M.: Implementation and Advanced Results on the Non-interrupted Skeletonization Algorithm. In: 9th CAIP Int. Conference on Computer Analysis of Images and Patterns, September 5-7, Warsaw (2001); Proceedings published in: Lecture Notes in Computer Science - Skarbek, W. (Ed.), Computer Analysis of Images and Patterns, pp. 601–609, Springer-Verlag Heidelberg, Berlin (2001)

    Google Scholar 

  5. Saeed, K.: Computer Graphics Analysis: A Method for Arbitrary Image Shape Description. MGV - International Journal on Machine Graphics and Vision, Institute of Computer Science 10(2), 185–194 (2001)

    Google Scholar 

  6. Saeed, K., Tabedzki, M., Adamski, M.: A New Approach for Object-Feature Extract and Recognition. In: 9th International Conference on Advanced Computer Systems - ACS 2002, Miedzyzdroje, October 23-25, pp. 389–397 (2002)

    Google Scholar 

  7. Saeed, K.: New Approaches for Cursive Languages Recognition: Machine and Hand Written Scripts and Texts. In: Advances in Neural Networks and Applications, pp. 92–97. WSES Press, Tenerife (2001)

    Google Scholar 

  8. Riedmiller, M., Braun, H.: RPROP - A Fast Adaptive Learning Algorithm, Technical Report, Karlsruhe University (1992)

    Google Scholar 

  9. Goraine, H.: Application of Neural Networks on Cursive Text Recognition. In: Advances in Signal Processing and Computer Technologies, pp. 350–354. WSES/IEEE, WSES Press (2001)

    Google Scholar 

  10. Tadeusiewicz, R.: Neural Networks. AOW, Cracow (1992) (in Polish)

    Google Scholar 

  11. Saeed, K., Kozlowski, M.: An Image-Based System for Spoken-Letter Recognition. In: Pietkov, M. (ed.) Computer Analysis of Images and Patterns. LNCS, pp. 494–502. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2004 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Saeed, K., Tabedzki, M. (2004). Cursive-Character Script Recognition Using Toeplitz Model and Neural Networks. In: Rutkowski, L., Siekmann, J.H., Tadeusiewicz, R., Zadeh, L.A. (eds) Artificial Intelligence and Soft Computing - ICAISC 2004. ICAISC 2004. Lecture Notes in Computer Science(), vol 3070. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24844-6_100

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-24844-6_100

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-22123-4

  • Online ISBN: 978-3-540-24844-6

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics