Open Access

ARTICLE

An Efficient Hybrid Model for Arabic Text Recognition

Hicham Lamtougui^1,*, Hicham El Moubtahij², Hassan Fouadi¹, Khalid Satori¹

1 LIIAN Laboratory, Faculty of Sciences Dhar-Mahraz, Fez, 30000, Morocco
2 Modeling, Systems and Technologies of Information Team, University of Ibn Zohr, Agadir

* Corresponding Author: Hicham Lamtougui. Email:

Computers, Materials & Continua 2023, 74(2), 2871-2888. https://doi.org/10.32604/cmc.2023.032550

Received 22 May 2022; Accepted 14 July 2022; Issue published 31 October 2022

Abstract

In recent years, Deep Learning models have become indispensable in several fields such as computer vision, automatic object recognition, and automatic natural language processing. The implementation of a robust and efficient handwritten text recognition system remains a challenge for the research community in this field, especially for the Arabic language, which, compared to other languages, has a dearth of published works. In this work, we presented an efficient and new system for offline Arabic handwritten text recognition. Our new approach is based on the combination of a Convolutional Neural Network (CNN) and a Bidirectional Long-Term Memory (BLSTM) followed by a Connectionist Temporal Classification layer (CTC). Moreover, during the training phase of the model, we introduce an algorithm of data augmentation to increase the quality of data. Our proposed approach can recognize Arabic handwritten texts without the need to segment the characters, thus overcoming several problems related to this point. To train and test (evaluate) our approach, we used two Arabic handwritten text recognition databases, which are IFN/ENIT and KHATT. The Experimental results show that our new approach, compared to other methods in the literature, gives better results.

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Keywords

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