Cursive Letters Language Processing: Muqla Model and Toeplitz Matrices Approach

  • Khalid Saeed
  • Agnieszka Dardzińska
Part of the Advances in Soft Computing book series (AINSC, volume 7)


This article presents a new model (based on Muqla idea) used in recognition of cursive letters. It is used for creating feature vectors based on Toeplitz matrices. As the approach is used for the first time, no comparison had been made from modelling point of view. However, the way of feature vectors extraction in this approach showed its simplicity and may find its practical applications in processing and verifying both written and spoken texts. The suggested algorithm makes it possible to reduce the limiting factors [1] in recognition as it is easy to extend it to cover more vocabulary, syntax or semantic information.


Characteristic Vector Automatic Speech Recognition Toeplitz Matrice Suggested Algorithm Human Language Technology 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. [1]
    R. A. Cole, Editor in Chief, “Survey of the State of the Art in Human Language Technology,” Cambridge University Press, 1996.Google Scholar
  2. [2]
    M. H. Hassan, R. Kurowski, “An Introductory Course to Arabic Language,” WP, Cracow 1972.Google Scholar
  3. [3]
    K. Saeed, R. Niedzielski, “A Fast Recognition Structured Algorithm of Typewritten Cursive Scripts,”Proceedings of 6th Conference of PTSK on Simulation in Research and Development(in Polish), pp. 67–71, BialystokBialowieza 1999.Google Scholar
  4. [4]
    K. Saeed, “A Projection Approach for Arabic Handwritten Characters Recognition,”Proc. of International Symposium on Computational Intelligence- ISCI Aug. 31st— Sep. 1stKosice 2000, Slovakia. (Accepted for publication and presentation).Google Scholar
  5. [5]
    K. Saeed, “On the realization of digital filters,”Proceedings of 1st International Conference on Digital Signal Processing and its ApplicationsDSPA’98,Vol. 1, pp. 141–143,Moscow, 1998.Google Scholar
  6. [6]
    K. Saeed, R. Niedzielski, “Experiments on Thinning of Cursive-Style Alphabets,” Inter. Conf. on Information Technologies ITESB’99June 24–25, Minsk 1999.Google Scholar
  7. [7]
    M. J. Nalewajko, “Universal system for recognition of scripts using artificial neural networks,” M.Sc. Thesis, Faculty of Computer Science Technical University of Bialystok,Bialystok, July 1999.Google Scholar
  8. [8]
    K. Saeed,“Three-Agent System for Cursive-Scripts Recognition,” Proc. CVPRIP ‘2000 Computer Vision,Pattern Recognition and Image Proc.The 5th Joint Conf. on Information Sciences JCIS2000,Feb. 27th — March 3rd 2000, New Jersey, USA.Google Scholar
  9. [9]
    Burr, "Experiments on Neural Net Recognition of Spoken and Written Text," IEEE Transactions on Acoustics, Speech, and Signal Processing, Vol.36,NO.7,July1988Google Scholar
  10. [10]
    M. Ghuwar, W. Skarbek, “Recognition of Arabic Characters - A Survey,” Polish Academy of Science, Manuscript no. 740, Warsaw 1994.Google Scholar
  11. [11]
    N. Ström and others, "Acoustic Modelling Improvements in a Segment-Based Speech Recognizer," Proc. of the 1999 IEEE Workshop on Automatic Speech Recognition and Understanding,Keystone, Colorado, USA.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Khalid Saeed
    • 1
  • Agnieszka Dardzińska
    • 1
  1. 1.Faculty of Computer Science Computer Engineering DepartmentBialystok University of TechnologyPoland

Personalised recommendations