An evolutionary neuro-fuzzy approach to recognize on-line Arabic handwriting

Author

Alimi, Adel M.

Author_Institution

Dept. of Electr. Eng., Ecole Nat. d´´Ingenieurs de Sfax, Tunisia

Volume

1

fYear

1997

fDate

18-20 Aug 1997

Firstpage

382

Abstract

The author describes a system that recognizes on-line Arabic cursive handwriting. In this system, a genetic algorithm is used to select the best combination of characters recognized by a fuzzy neural network. The handwritten words used in this system are modelled by a theory of movement generation. Based on this motor theory, the features extracted from each character are the neuro-physiological and biomechanical parameters of the equation describing the curvilinear velocity of the script. The evolutionary approach proposed permits the recognition of cursive handwriting with a segmentation procedure allowing overlapped strokes having neuro-physiological meaning

Keywords

biomechanics; character recognition; character sets; feature extraction; fuzzy neural nets; genetic algorithms; image segmentation; neurophysiology; biomechanical parameters; character combination; curvilinear script velocity; evolutionary neuro-fuzzy approach; feature extraction; fuzzy neural network; genetic algorithm; handwritten words; motor theory; movement generation theory; neurophysiological parameters; on-line Arabic cursive handwriting recognition; overlapped strokes; segmentation procedure; Character recognition; Equations; Feature extraction; Fuzzy neural networks; Genetic algorithms; Handwriting recognition; Machine intelligence; Shape; System testing; Writing;

fLanguage

English

Publisher

ieee

Conference_Titel

Document Analysis and Recognition, 1997., Proceedings of the Fourth International Conference on

Conference_Location

Ulm

Print_ISBN

0-8186-7898-4

Type

conf

DOI

10.1109/ICDAR.1997.619875

Filename

619875