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Second International Conference on Document Image Analysis for Libraries (DIAL'06)
Combining a hybrid Approach for Features Selection and Hidden Markov Models in Multifont Arabic Characters Recognition
Lyon, France
April 27-April 28
ISBN: 0-7695-2531-8
ASCII Text
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Nadia Ben Amor, Najoua Essoukri Ben Amara, "Combining a hybrid Approach for Features Selection and Hidden Markov Models in Multifont Arabic Characters Recognition," Document Image Analysis for Libraries, International Workshop on, pp. 103-107, Second International Conference on Document Image Analysis for Libraries (DIAL'06), 2006.
 
 
BibTex
x
 
@article{ 10.1109/DIAL.2006.7,
author = {Nadia Ben Amor and Najoua Essoukri Ben Amara},
title = {Combining a hybrid Approach for Features Selection and Hidden Markov Models in Multifont Arabic Characters Recognition},
journal ={Document Image Analysis for Libraries, International Workshop on},
volume = {0},
year = {2006},
isbn = {0-7695-2531-8},
pages = {103-107},
doi = {http://doi.ieeecomputersociety.org/10.1109/DIAL.2006.7},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - CONF
JO - Document Image Analysis for Libraries, International Workshop on
TI - Combining a hybrid Approach for Features Selection and Hidden Markov Models in Multifont Arabic Characters Recognition
SN - 0-7695-2531-8
SP103
EP107
A1 - Nadia Ben Amor,
A1 - Najoua Essoukri Ben Amara,
PY - 2006
KW - null
VL - 0
JA - Document Image Analysis for Libraries, International Workshop on
ER -
 
Nadia Ben Amor, National Engineering School of Tunis (ENIT) Tunisia
Najoua Essoukri Ben Amara, National Engineering School of Sousse (ENISo) Tunisia

Optical Characters Recognition (OCR) has been an active subject of research since the early days of computers. Despite the age of the subject, it remains one of the most challenging and exciting areas of research in computer science. In recent years it has grown into a mature discipline, producing a huge body of work.

In this paper, we present an Arabic Optical multifont Character Recognition approach based on both Hough transform and wavelets transform for features selection and Hidden Markov Models for classification In the next sections, the whole OCR system will be presented. The different tests carried out on a set of about 170.000 samples of multifont Arabic characters and the obtained results so far will be developed.

Citation:
Nadia Ben Amor, Najoua Essoukri Ben Amara, "Combining a hybrid Approach for Features Selection and Hidden Markov Models in Multifont Arabic Characters Recognition," dial, pp.103-107, Second International Conference on Document Image Analysis for Libraries (DIAL'06), 2006
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