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Arabic handwriting recognition system using convolutional neural network
- 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.
- Subjects :
- Character (computing)
Arabic
Computer science
business.industry
Arabic handwriting recognition
020206 networking & telecommunications
02 engineering and technology
computer.software_genre
Convolutional neural network
language.human_language
Artificial Intelligence
Handwriting recognition
Handwriting
Component (UML)
0202 electrical engineering, electronic engineering, information engineering
language
020201 artificial intelligence & image processing
Artificial intelligence
business
computer
Software
Natural language processing
Subjects
Details
- ISSN :
- 14333058 and 09410643
- Volume :
- 33
- Database :
- OpenAIRE
- Journal :
- Neural Computing and Applications
- Accession number :
- edsair.doi...........d965f003d5df76a5a9ade2c0e7a8094e