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Arabic handwriting recognition system using convolutional neural network

Authors :
Najwa Altwaijry
Isra Al-Turaiki
Source :
Neural Computing and Applications. 33:2249-2261
Publication Year :
2020
Publisher :
Springer Science and Business Media LLC, 2020.

Abstract

Automatic handwriting recognition is an important component for many applications in various fields. It is a challenging problem that has received a lot of attention in the past three decades. Research has focused on the recognition of Latin languages’ handwriting. Fewer studies have been done for the Arabic language. In this paper, we present a new dataset of Arabic letters written exclusively by children aged 7–12 which we call Hijja. Our dataset contains 47,434 characters written by 591 participants. In addition, we propose an automatic handwriting recognition model based on convolutional neural networks (CNN). We train our model on Hijja, as well as the Arabic Handwritten Character Dataset (AHCD) dataset. Results show that our model’s performance is promising, achieving accuracies of 97% and 88% on the AHCD dataset and the Hijja dataset, respectively, outperforming other models in the literature.

Details

ISSN :
14333058 and 09410643
Volume :
33
Database :
OpenAIRE
Journal :
Neural Computing and Applications
Accession number :
edsair.doi...........d965f003d5df76a5a9ade2c0e7a8094e