Temporal Neural System Applied to Arabic Online Characters Recognition
Khadidja Belbachir1
and Redouane Tlemsani2
1Department of computer sciences, University of
Science and Technologies of Oran Mohamed Boudiaf, Algeria
2LaRATIC
Laboratory, National Institute of Telecommunication an ICTs of Oran, Algeria
Abstract: This work presents survey, implementation and test
for a neural network: Time Delay Neural Network (TDNN), applied to on-line
handwritten recognition characters. In this work, we present a recognizer
conception for on-line Arabic handwriting. On-line handwriting recognition of
Arabic script is a complex problem, since it is naturally both cursive and
unconstrained. This system permits to interpret a script represented by the pen
trajectory. This technique is used notably in the electronic tablets. We will
construct a data base with several scripters. Afterwards, and before attacking
the recognition phase, there is a constructional samples phase of Arabic
characters acquired from an electronic tablet to digitize Noun Database.
Obtained scores shows an effectiveness of the proposed approach based on
convolutional neural networks.
Keywords: Isolated handwritten characters recognition,
on-line recognition, convolution neural network, TDNN.