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Arabic handwriting recognition system using convolutional neural network

Neural computing & applications, 2021-04, Vol.33 (7), p.2249-2261 [Peer Reviewed Journal]

The Author(s) 2020 ;The Author(s) 2020. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 0941-0643 ;EISSN: 1433-3058 ;DOI: 10.1007/s00521-020-05070-8

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Title:
Arabic handwriting recognition system using convolutional neural network
Author: Altwaijry, Najwa ; Al-Turaiki, Isra
Subjects: Artificial Intelligence ; Artificial neural networks ; Computational Biology/Bioinformatics ; Computational Science and Engineering ; Computer Science ; Data Mining and Knowledge Discovery ; Datasets ; Handwriting ; Handwriting recognition ; Image Processing and Computer Vision ; Model accuracy ; Neural networks ; Original Article ; Probability and Statistics in Computer Science
Is Part Of: Neural computing & applications, 2021-04, Vol.33 (7), p.2249-2261
Description: 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.
Publisher: London: Springer London
Language: English
Identifier: ISSN: 0941-0643
EISSN: 1433-3058
DOI: 10.1007/s00521-020-05070-8
Source: Springer Nature OA/Free Journals
ProQuest Central

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Arabic handwriting recognition system using convolutional neural network

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