Authors:
Hasanien Alothman
1
;
2
;
3
;
Wafa Lejmi
1
;
3
and
Mohamed Ali Mahjoub
3
Affiliations:
1
ISITCom, Higher Institute of Computer Science and Communication Technologies of Sousse, University of Sousse, 4011 Sousse, Tunisia
;
2
College of Education for Pure Science, Computer Science Department, University of Mosul, Iraq
;
3
LATIS - Laboratory of Advanced Technology and Intelligent Systems, University of Sousse, 4011 Sousse, Tunisia
Keyword(s):
Handwriting, Arabic, Script, Text, Character, Descriptor, Substantial Derivative, Feature, Extraction, Acceleration, ADAB Dataset, Recognition, LSTM.
Abstract:
As some tasks easily performed by man seem to be hard to be accomplished by the machine, the Artificial Intelligence field examines more and more the reproduction of thinking methods and human intuition by studying some mental faculties and substituting them by calculating approaches. Among the major fields of such interest, we can focus on recognizing handwritten characters. However, most handwritten characters are written in Latin, which makes the recognition of Arabic characters handwriting a delicate process compared to other languages, due to the specificity of Arabic words. In this paper, we aim to conceive a framework that offers the ability to recognize online Arabic handwriting applied to a dataset named ADAB (Arabic DAtaBase), using a particular descriptor based on a substantial derivative and a neural network handling Arabic handwritten characters features and then electing the appropriate output for the final decision.