Recognition of Off-Line Handwritten Arabic Words Using Neural Network

Author

Alma´adeed, Somaya

Author_Institution

Dept. of Comput. Sci. & Eng., Qatar Univ., Doha

fYear

1993

fDate

16-18 Aug. 1993

Firstpage

141

Lastpage

144

Abstract

Neural network (NN) has been used with some success in recognizing printed Arabic words. In this paper, a complete scheme for unconstrained Arabic handwritten word recognition based on a neural network is proposed and discussed. The overall engine of this combination of a global feature scheme with a NN is a system able to classify Arabic-handwritten words of one hundred different writers. The system first attempts to remove some of the variation in the images that do not affect the identity of the handwritten word. Next, the system codes the skeleton and edge of the word so that feature information about the strokes in the skeleton is extracted. Then, a classification process based on the artificial NN classifier is used as global recognition engine, to classify the Arabic words. The output is a word in the dictionary. A detailed experiment is carried out, and successful recognition results are reported

Keywords

feature extraction; handwritten character recognition; natural languages; neural nets; pattern classification; Arabic handwritten word recognition; feature extraction; global recognition engine; image classification; neural network; Computer science; Data mining; Feature extraction; Handwriting recognition; Image recognition; Image segmentation; Neural networks; Neurons; Solid modeling; Writing;

fLanguage

English

Publisher

ieee

Conference_Titel

Geometric Modeling and Imaging--New Trends, 2006

Conference_Location

London, England

Print_ISBN

0-7695-2604-7

Type

conf

DOI

10.1109/GMAI.2006.43

Filename

1648757