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Deep Learning-Based Child Handwritten Arabic Character Recognition and Handwriting Discrimination

Handwritten Arabic character recognition has received increasing research interest in recent years. However, as of yet, the majority of the existing handwriting recognition systems have only focused on adult handwriting. In contrast, there have not been many studies conducted on child handwriting, n...

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Autores principales: Alwagdani, Maram Saleh, Jaha, Emad Sami
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10422599/
https://www.ncbi.nlm.nih.gov/pubmed/37571556
http://dx.doi.org/10.3390/s23156774
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author Alwagdani, Maram Saleh
Jaha, Emad Sami
author_facet Alwagdani, Maram Saleh
Jaha, Emad Sami
author_sort Alwagdani, Maram Saleh
collection PubMed
description Handwritten Arabic character recognition has received increasing research interest in recent years. However, as of yet, the majority of the existing handwriting recognition systems have only focused on adult handwriting. In contrast, there have not been many studies conducted on child handwriting, nor has it been regarded as a major research issue yet. Compared to adults’ handwriting, children’s handwriting is more challenging since it often has lower quality, higher variation, and larger distortions. Furthermore, most of these designed and currently used systems for adult data have not been trained or tested for child data recognition purposes or applications. This paper presents a new convolution neural network (CNN) model for recognizing children’s handwritten isolated Arabic letters. Several experiments are conducted here to investigate and analyze the influence when training the model with different datasets of children, adults, and both to measure and compare performance in recognizing children’s handwritten characters and discriminating their handwriting from adult handwriting. In addition, a number of supplementary features are proposed based on empirical study and observations and are combined with CNN-extracted features to augment the child and adult writer-group classification. Lastly, the performance of the extracted deep and supplementary features is evaluated and compared using different classifiers, comprising Softmax, support vector machine (SVM), k-nearest neighbor (KNN), and random forest (RF), as well as different dataset combinations from Hijja for child data and AHCD for adult data. Our findings highlight that the training strategy is crucial, and the inclusion of adult data is influential in achieving an increased accuracy of up to around 93% in child handwritten character recognition. Moreover, the fusion of the proposed supplementary features with the deep features attains an improved performance in child handwriting discrimination by up to around 94%.
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spelling pubmed-104225992023-08-13 Deep Learning-Based Child Handwritten Arabic Character Recognition and Handwriting Discrimination Alwagdani, Maram Saleh Jaha, Emad Sami Sensors (Basel) Article Handwritten Arabic character recognition has received increasing research interest in recent years. However, as of yet, the majority of the existing handwriting recognition systems have only focused on adult handwriting. In contrast, there have not been many studies conducted on child handwriting, nor has it been regarded as a major research issue yet. Compared to adults’ handwriting, children’s handwriting is more challenging since it often has lower quality, higher variation, and larger distortions. Furthermore, most of these designed and currently used systems for adult data have not been trained or tested for child data recognition purposes or applications. This paper presents a new convolution neural network (CNN) model for recognizing children’s handwritten isolated Arabic letters. Several experiments are conducted here to investigate and analyze the influence when training the model with different datasets of children, adults, and both to measure and compare performance in recognizing children’s handwritten characters and discriminating their handwriting from adult handwriting. In addition, a number of supplementary features are proposed based on empirical study and observations and are combined with CNN-extracted features to augment the child and adult writer-group classification. Lastly, the performance of the extracted deep and supplementary features is evaluated and compared using different classifiers, comprising Softmax, support vector machine (SVM), k-nearest neighbor (KNN), and random forest (RF), as well as different dataset combinations from Hijja for child data and AHCD for adult data. Our findings highlight that the training strategy is crucial, and the inclusion of adult data is influential in achieving an increased accuracy of up to around 93% in child handwritten character recognition. Moreover, the fusion of the proposed supplementary features with the deep features attains an improved performance in child handwriting discrimination by up to around 94%. MDPI 2023-07-28 /pmc/articles/PMC10422599/ /pubmed/37571556 http://dx.doi.org/10.3390/s23156774 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Alwagdani, Maram Saleh
Jaha, Emad Sami
Deep Learning-Based Child Handwritten Arabic Character Recognition and Handwriting Discrimination
title Deep Learning-Based Child Handwritten Arabic Character Recognition and Handwriting Discrimination
title_full Deep Learning-Based Child Handwritten Arabic Character Recognition and Handwriting Discrimination
title_fullStr Deep Learning-Based Child Handwritten Arabic Character Recognition and Handwriting Discrimination
title_full_unstemmed Deep Learning-Based Child Handwritten Arabic Character Recognition and Handwriting Discrimination
title_short Deep Learning-Based Child Handwritten Arabic Character Recognition and Handwriting Discrimination
title_sort deep learning-based child handwritten arabic character recognition and handwriting discrimination
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10422599/
https://www.ncbi.nlm.nih.gov/pubmed/37571556
http://dx.doi.org/10.3390/s23156774
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