A Deep Convolutional and Recurrent Approach for Large Vocabulary Arabic Word Recognition

Faten Ziadi, Faten Ziadi, Imen Ben Cheikh, Mohamed Jemni

2022

Abstract

In this paper, we propose a convolutional recurrent approach for Arabic word recognition. We handle a large vocabulary of Arabic decomposable words, which are factored according to their roots and schemes. Exploiting derivational morphology, we have conceived as the first step a convolutional neural network, which classifies Arabic roots extracted from a set of word samples int the APTI database. In order to further exploit linguistic knowledge, we have accomplished the word recognition process through a recurrent network, especially LSTM. Thanks to its recurrence and memory cabability, the LSTM model focuses not only prefixes, infixes and suffixes listed in chronological order, but also on the relation between them in order to recognize word patterns and some flexional details such as, gender, number, tense, etc.

Paper Citation

in Harvard Style

Ziadi F., Ben Cheikh I. and Jemni M. (2022). A Deep Convolutional and Recurrent Approach for Large Vocabulary Arabic Word Recognition. In Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-549-4, pages 213-220. DOI: 10.5220/0010814800003122

in Bibtex Style

@conference{icpram22,
author={Faten Ziadi and Imen Ben Cheikh and Mohamed Jemni},
title={A Deep Convolutional and Recurrent Approach for Large Vocabulary Arabic Word Recognition},
booktitle={Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2022},
pages={213-220},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010814800003122},
isbn={978-989-758-549-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - A Deep Convolutional and Recurrent Approach for Large Vocabulary Arabic Word Recognition
SN - 978-989-758-549-4
AU - Ziadi F.
AU - Ben Cheikh I.
AU - Jemni M.
PY - 2022
SP - 213
EP - 220
DO - 10.5220/0010814800003122