Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 30073
A New Pattern for Handwritten Persian/Arabic Digit Recognition

Authors: A. Harifi, A. Aghagolzadeh


The main problem for recognition of handwritten Persian digits using Neural Network is to extract an appropriate feature vector from image matrix. In this research an asymmetrical segmentation pattern is proposed to obtain the feature vector. This pattern can be adjusted as an optimum model thanks to its one degree of freedom as a control point. Since any chosen algorithm depends on digit identity, a Neural Network is used to prevail over this dependence. Inputs of this Network are the moment of inertia and the center of gravity which do not depend on digit identity. Recognizing the digit is carried out using another Neural Network. Simulation results indicate the high recognition rate of 97.6% for new introduced pattern in comparison to the previous models for recognition of digits.

Keywords: Pattern recognition, Persian digits, NeuralNetwork.

Digital Object Identifier (DOI):

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1295


[1] A. Mowlaei, K. Faez and A. T. Haghighat, "Feature extraction with Wavelet Transform for recognition of isolated handwritten Farsi/Arabic character an numerals", 14th International Conference on Digital Signal Processing, vol. 2, 1-3 July 2002
[2] M. H. Shirali, K. Faez and A. Khotanzad, "Recognition of handwritten Persian/Arabic numerals by shadow coding and an edited probabilistic Neural Network", International Conference on Image Processing, vol. 3, 23-26 October 1995
[3] H. M. M. Hosseini and A. Bouzerdoum, "A combined method for Persian and Arabic handwritten digit recognition", Australian New Zealand conf. on Intelligent Information Systems, Adelaide, Australia, 18-20 Nov. 1996.
[4] D. J. Burr, "Experiments on Neural Net recognition of spoken and written text," IEEE Transactions on Acoustic, Speech, and Signal Processing, Vol. 36, No. 7, pp. 1162-1168, July 1988.
[5] S. Haykin, "Neural networks a comprehensive foundation", second edition, Prentice-Hall, Inc, 1999.