A NEW HYBRID SYSTEM FOR RECOGNITION OF HANDWRITTEN-SCRIPT

Authors

  • Khalid Saeed
  • Marek Tabedzki

DOI:

https://doi.org/10.47839/ijc.3.1.253

Keywords:

Object, Letter, word Recognition, Minimal Eigenvalues, Neural Networks

Abstract

A new method for object recognition and classification is presented in this paper. It merges two well-known and tested methods: neural networks and method of minimal eigenvalues. Each of these methods answers for a different part of recognition process. Method of minimal eigenvalues makes preparatory stage of analysis – of coordinates of characteristic points we get the vector describing given image. Next, it is recognized and classified with neural network. Gathering of characteristic points we perform with our view-based algorithm, but other methods should also do. In this work, method was applied for words in Latin alphabet – handwritten and machine-printed. The obtained results are promising.

References

B. Widrow. R. Winter. R. Baxter. Layered Neural Nets for Pattern Recognition. IEEE Trans. ASSP, 1988, Vol. 36, pp. 1109-1118.

K. Saeed. Computer Graphics Analysis: A Method for Arbitrary Image Shape Description. Volume 10, Number 2, 2001, pp. 185-194, MGV - International Journal on Machine Graphics and Vision, Institute of Computer Science. Polish Academy of Sciences. Warsaw 2001.

K. Saeed. M. Tabedzki. M. Adamski. A New Approach for Object-Feature Extract and Recognition. 9th International Conference on Advanced Computer Systems – ACS’02. Miedzyzdroje, 23-25 October 2002. pp. 389-397.

K. Saeed. Object Classification and Recognition Using Toeplitz Matrices. Artificial Intelligence and Security in Computing Systems. Edited by Jerzy Soldek and Leszek Drobiazgiewicz. The Kluwer International Series in Engineering and Computer Science, Volume 752. September 2003.

R. C. Gonzalez and R. E Woods. Digital Image Processing. Addison-Wesley Publishing Company, 1992. pp. 7-9, 413-414.

K. Bowyer and N. Ahuja. Advances in Image Understanding. IEEE Computer Society Press, 1996. p. 65.

V. F. Leavers. Shape Detection in Computer Vision Using the Hough Transform. Springer-Verlag, 1992.

M. Rybnik. A. Chebira. K. Madani. K. Saeed. M. Tabedzki. M. Adamski. A Hybrid Neural-Based Information-Processing Approach Combining a View-Based Feature Extractor and a Treelike Intelligent Classifier. CISIM – Computer Information Systems and Industrial Management Applications. WSFiZ Press. Bialystok, 2003.

K. Saeed. A New Approach in Image Classification. Proc. 5th International Conference on Digital Signal Processing and its Applications - DSPA'03. Moscow 2003. Vol. 1, pp. 49-52.

S. I. Amari. Mathematical theory of neural learning. New Generation Computing. 1991. Vol. 8, pp. 281-294.

S. Haykin. Neural Networks, a Comprehensive Foundation. Macmillan College Publishing Company. New York 1994.

J. Hertz. A. Krogh. R. G. Palmer. Introduction to Theory of Neural Computing. WNT, Warsaw 1995.

K. Saeed, M. Tabedzki, "Hybrid System for Letter Recognition," Third International Conference on Neural Networks and Artificial Intelligence - ICNNAI’03, pp. 44 - 48, 12-14 November, Minsk 2003.

N. Vapnik V. Principle of risk minimization for learning theory. NIPS4. Eds. J. Moody, S. Hanson, R. Lippmann. Morgan Kaufmann, San Mateo 1992, pp. 831-838.

R. Hecht-Nielsen. Neurocomputing. Addison Wesley. Amsterdam, 1991.

K. Saeed et al, "Language Processing: Word Recognition without Segmentation," JASIST - Journal of the American Society for Information Science and Technology, Volume 52, Issue 14, 2001, pp. 1275-1279, John Wiley and Sons, New York 2001.

K. Saeed, M. Kozlowski, "An Image-Based System for Spoken-Letter Recognition,” 10th Int. Conference CAIP’03 on Computer Analysis of Images and Patterns, August 2003, Groningen. Proceedings published in: Lecture Notes in Computer Science, Petkov and Westenberg (Eds.), pp. 494-502, LNCS 2756, Springer-Verlag Heidelberg: Berlin 2003.

K. Saeed, M. Nammous, “Spoken Arabic-Digit Recognition,” Submitted to ACS-CISIM’04, WSFiZ Press.

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Published

2014-08-01

How to Cite

Saeed, K., & Tabedzki, M. (2014). A NEW HYBRID SYSTEM FOR RECOGNITION OF HANDWRITTEN-SCRIPT. International Journal of Computing, 3(1), 50-57. https://doi.org/10.47839/ijc.3.1.253

Issue

2004, Volume 3, Issue 1

Section

Articles

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