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An intelligent approach for Arabic handwritten letter recognition using convolutional neural network
Currently, digital transformation has occurred in most countries in the world to varying degrees, but digitizing business processes are complex in terms of understanding the various aspects of manual documentation. The use of digital devices and intelligent systems is vital in the digital transforma...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
PeerJ Inc.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9202630/ https://www.ncbi.nlm.nih.gov/pubmed/35721403 http://dx.doi.org/10.7717/peerj-cs.995 |
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author | Ullah, Zahid Jamjoom, Mona |
author_facet | Ullah, Zahid Jamjoom, Mona |
author_sort | Ullah, Zahid |
collection | PubMed |
description | Currently, digital transformation has occurred in most countries in the world to varying degrees, but digitizing business processes are complex in terms of understanding the various aspects of manual documentation. The use of digital devices and intelligent systems is vital in the digital transformation of manual documentation from hardcopy to digital formats. The transformation of handwritten documents into electronic files is one of the principal aspects of digitization and represents a common need shared by today’s businesses. Generally, handwriting recognition poses a complex digitization challenge, and Arabic handwriting recognition, specifically, proves inordinately challenging due to the nature of Arabic scripts and the excessive diversity in human handwriting. This study presents an intelligent approach for recognizing handwritten Arabic letters. In this approach, a convolution neural network (CNN) model is proposed to recognize handwritten Arabic letters. The model is regularized using batch normalization and dropout operations. Moreover, the model was tested with and without dropout, resulting in a significant difference in the performance. Hence, the model overfitting has been prevented using dropout regularization. The proposed model was applied to the prominent, publicly-available Arabic handwritten characters (AHCD) dataset with 16,800 letters, and the performance was measured using several evaluation measures. The experimental results show the best fit of the proposed model in terms of higher accuracy results that reached 96.78%; additionally, other evaluation measures compared to popular domain-relevant approaches in the literature. |
format | Online Article Text |
id | pubmed-9202630 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-92026302022-06-17 An intelligent approach for Arabic handwritten letter recognition using convolutional neural network Ullah, Zahid Jamjoom, Mona PeerJ Comput Sci Algorithms and Analysis of Algorithms Currently, digital transformation has occurred in most countries in the world to varying degrees, but digitizing business processes are complex in terms of understanding the various aspects of manual documentation. The use of digital devices and intelligent systems is vital in the digital transformation of manual documentation from hardcopy to digital formats. The transformation of handwritten documents into electronic files is one of the principal aspects of digitization and represents a common need shared by today’s businesses. Generally, handwriting recognition poses a complex digitization challenge, and Arabic handwriting recognition, specifically, proves inordinately challenging due to the nature of Arabic scripts and the excessive diversity in human handwriting. This study presents an intelligent approach for recognizing handwritten Arabic letters. In this approach, a convolution neural network (CNN) model is proposed to recognize handwritten Arabic letters. The model is regularized using batch normalization and dropout operations. Moreover, the model was tested with and without dropout, resulting in a significant difference in the performance. Hence, the model overfitting has been prevented using dropout regularization. The proposed model was applied to the prominent, publicly-available Arabic handwritten characters (AHCD) dataset with 16,800 letters, and the performance was measured using several evaluation measures. The experimental results show the best fit of the proposed model in terms of higher accuracy results that reached 96.78%; additionally, other evaluation measures compared to popular domain-relevant approaches in the literature. PeerJ Inc. 2022-05-27 /pmc/articles/PMC9202630/ /pubmed/35721403 http://dx.doi.org/10.7717/peerj-cs.995 Text en ©2022 Ullah and Jamjoom https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited. |
spellingShingle | Algorithms and Analysis of Algorithms Ullah, Zahid Jamjoom, Mona An intelligent approach for Arabic handwritten letter recognition using convolutional neural network |
title | An intelligent approach for Arabic handwritten letter recognition using convolutional neural network |
title_full | An intelligent approach for Arabic handwritten letter recognition using convolutional neural network |
title_fullStr | An intelligent approach for Arabic handwritten letter recognition using convolutional neural network |
title_full_unstemmed | An intelligent approach for Arabic handwritten letter recognition using convolutional neural network |
title_short | An intelligent approach for Arabic handwritten letter recognition using convolutional neural network |
title_sort | intelligent approach for arabic handwritten letter recognition using convolutional neural network |
topic | Algorithms and Analysis of Algorithms |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9202630/ https://www.ncbi.nlm.nih.gov/pubmed/35721403 http://dx.doi.org/10.7717/peerj-cs.995 |
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