D.B. Megherbi, S.M. Lodhi, and J.A. Boulenouar (USA)
Neural Networks, Signals & Low Level Image Processing, Document Decoding, Filtering, Image Enhancement, Segmentation, Coding.
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 a classification of 36 Urdu characters into seven sub-classes namely sub-classes 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. 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.
Important Links:
Go Back
