Handwriting Image Classification for Automated Diagnosis of Learning Disabilities: A Review on Deep Learning Models and Future Directions

Handwriting Image Classification for Automated Diagnosis of Learning Disabilities: A Review on Deep Learning Models and Future Directions

This study reviews deep learning models used in handwriting image classification for the automated diagnosis of learning disabilities. By addressing handwriting diversity and misclassification challenges, two models were highlighted: Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs...

Full description

Bibliographic Details
Published in:19th International Joint Symposium on Artificial Intelligence and Natural Language Processing, iSAI-NLP 2024
Main Author: Sukiman S.A.; Husin N.A.; Hamdan H.; Murad M.A.A.
Format: Conference paper
Language:English
Published: Institute of Electrical and Electronics Engineers Inc. 2024
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85216562911&doi=10.1109%2fiSAI-NLP64410.2024.10799245&partnerID=40&md5=94ad7c5e822a95e47c2061ced489ffc5
id 2-s2.0-85216562911
spelling 2-s2.0-85216562911
Sukiman S.A.; Husin N.A.; Hamdan H.; Murad M.A.A.
Handwriting Image Classification for Automated Diagnosis of Learning Disabilities: A Review on Deep Learning Models and Future Directions
2024
19th International Joint Symposium on Artificial Intelligence and Natural Language Processing, iSAI-NLP 2024


10.1109/iSAI-NLP64410.2024.10799245
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85216562911&doi=10.1109%2fiSAI-NLP64410.2024.10799245&partnerID=40&md5=94ad7c5e822a95e47c2061ced489ffc5
This study reviews deep learning models used in handwriting image classification for the automated diagnosis of learning disabilities. By addressing handwriting diversity and misclassification challenges, two models were highlighted: Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs). Literature was retrieved from major databases including IEEE Xplore, Scopus, Web of Science (WoS), and Google Scholar, with studies on Parkinson's disease, tremor patients, and machine learning excluded. CNNs represent a more mature architecture focusing on convolutions, pooling, and activation function. Meanwhile, ViTs emerges as a promising alternative via its multi-head attention architecture. This review also compares the accuracy of both models, specifying the sources of handwriting images, as well as providing future directions relevant to the research field. © 2024 IEEE.
Institute of Electrical and Electronics Engineers Inc.

English
Conference paper
author Sukiman S.A.; Husin N.A.; Hamdan H.; Murad M.A.A.
spellingShingle Sukiman S.A.; Husin N.A.; Hamdan H.; Murad M.A.A.
Handwriting Image Classification for Automated Diagnosis of Learning Disabilities: A Review on Deep Learning Models and Future Directions
author_facet Sukiman S.A.; Husin N.A.; Hamdan H.; Murad M.A.A.
author_sort Sukiman S.A.; Husin N.A.; Hamdan H.; Murad M.A.A.
title Handwriting Image Classification for Automated Diagnosis of Learning Disabilities: A Review on Deep Learning Models and Future Directions
title_short Handwriting Image Classification for Automated Diagnosis of Learning Disabilities: A Review on Deep Learning Models and Future Directions
title_full Handwriting Image Classification for Automated Diagnosis of Learning Disabilities: A Review on Deep Learning Models and Future Directions
title_fullStr Handwriting Image Classification for Automated Diagnosis of Learning Disabilities: A Review on Deep Learning Models and Future Directions
title_full_unstemmed Handwriting Image Classification for Automated Diagnosis of Learning Disabilities: A Review on Deep Learning Models and Future Directions
title_sort Handwriting Image Classification for Automated Diagnosis of Learning Disabilities: A Review on Deep Learning Models and Future Directions
publishDate 2024
container_title 19th International Joint Symposium on Artificial Intelligence and Natural Language Processing, iSAI-NLP 2024
container_volume
container_issue
doi_str_mv 10.1109/iSAI-NLP64410.2024.10799245
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85216562911&doi=10.1109%2fiSAI-NLP64410.2024.10799245&partnerID=40&md5=94ad7c5e822a95e47c2061ced489ffc5
description This study reviews deep learning models used in handwriting image classification for the automated diagnosis of learning disabilities. By addressing handwriting diversity and misclassification challenges, two models were highlighted: Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs). Literature was retrieved from major databases including IEEE Xplore, Scopus, Web of Science (WoS), and Google Scholar, with studies on Parkinson's disease, tremor patients, and machine learning excluded. CNNs represent a more mature architecture focusing on convolutions, pooling, and activation function. Meanwhile, ViTs emerges as a promising alternative via its multi-head attention architecture. This review also compares the accuracy of both models, specifying the sources of handwriting images, as well as providing future directions relevant to the research field. © 2024 IEEE.
publisher Institute of Electrical and Electronics Engineers Inc.
issn
language English
format Conference paper
accesstype
record_format scopus
collection Scopus
_version_ 1825722578778456064

Similar Items