A novel fuzzy approach for handwritten arabic character recognition
No Thumbnail Available
Date
2015
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Springer
Abstract
The aim of our work is to present a new method
based on structural characteristics and a fuzzy classifier for
off-line recognition of handwritten Arabic characters in all
their forms (beginning, end, middle and isolated). The
proposed method can be integrated in any handwritten
Arabic words recognition system based on an explicit
segmentation process. First, three preprocessing operations
are applied on character images: thinning, contour tracing
and connected components detection. These operations
extract structural characteristics used to divide the set of
characters into five subsets. Next, features are extracted
using invariant pseudo-Zernike moments. Classification
was done using the Fuzzy ARTMAP neural network, which
is very fast in training and supports incremental learning.
Five Fuzzy ARTMAP neural networks were employed;
each one is designed to recognize one subset of characters.
The recognition process is achieved in two steps: in the first
one, a clustering method affects characters to one of the
five character subsets. In the second one, the pseudo-Zernike
features are used by the appropriate Fuzzy ARTMAP
classifier to identify the character. Training process and tests were performed on a set of character images manually
extracted from the IFN/ENIT database. A height recognition
rate was reported.
Description
Keywords
Off-line character recognition, Handwritten Arabic, Pseudo-Zernike moments, Fuzzy ARTMAP