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Proceedings Paper

Two-stage neural-network-based technique for Urdu character two-dimensional shape representation, classification, and recognition
Author(s): Dalila B. Megherbi; S. M. Lodhi; A. J. Boulenouar
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Paper Abstract

This work is in the field of automated document processing. This work addresses the problem of representation and recognition of Urdu characters using Fourier representation and a Neural Network architecture. In particular, we show that a two-stage Neural Network scheme is used here to make classification of 36 Urdu characters into seven sub-classes namely subclasses characterized by seven proposed and defined fuzzy features specifically related to Urdu characters. We show that here Fourier Descriptors and Neural Network provide a remarkably simple way to draw definite conclusions from vague, ambiguous, noisy or imprecise information. In particular, we illustrate the concept of interest regions and describe a framing method that provides a way to make the proposed technique for Urdu characters recognition robust and invariant to scaling and translation. We also show that a given character rotation is dealt with by using the Hotelling transform. This transform is based upon the eigenvalue decomposition of the covariance matrix of an image, providing a method of determining the orientation of the major axis of an object within an image. Finally experimental results are presented to show the power and robustness of the proposed two-stage Neural Network based technique for Urdu character recognition, its fault tolerance, and high recognition accuracy.

Paper Details

Date Published: 21 March 2001
PDF: 13 pages
Proc. SPIE 4390, Applications and Science of Computational Intelligence IV, (21 March 2001); doi: 10.1117/12.421157
Show Author Affiliations
Dalila B. Megherbi, Univ. of Massachusetts/Lowell (United States)
S. M. Lodhi, Univ. of Massachusetts/Lowell (United States)
A. J. Boulenouar, Univ. of Massachusetts/Lowell (United States)


Published in SPIE Proceedings Vol. 4390:
Applications and Science of Computational Intelligence IV
Kevin L. Priddy; Paul E. Keller; Peter J. Angeline, Editor(s)

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