Handwriting Recognition in Down Syndrome Learners Using Deep Learning Methods

Kirsty-Lee Walker, Tevin Moodley

2023

Abstract

The Handwriting task is essential for any learner to develop as it can be seen as the gateway to further academic progression. The classification of Handwriting in learners with down syndrome is a relatively unexplored research area that has relied on manual techniques to monitor handwriting development. According to earlier studies, there is a gap in how down syndrome learners receive feedback on handwriting assignments, which hinders their academic progression. This research paper employs three deep learning architectures, VGG16, InceptionV2, and Xception, as end-to-end methods to categorise Handwriting as down syndrome or non-down syndrome. The InceptionV2 architecture correctly identifies an image with a model accuracy score of 99.62%. The results illustrate the manner in which the InceptionV2 architecture is able to classify Handwriting from learners with down syndrome accurately. This research paper advances the knowledge of which features differentiate a down syndrome learner’s Handwriting from a non-down syndrome learner’s Handwriting.

Paper Citation

in Harvard Style

Walker K. and Moodley T. (2023). Handwriting Recognition in Down Syndrome Learners Using Deep Learning Methods. In Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 4: VISAPP; ISBN 978-989-758-634-7, SciTePress, pages 936-943. DOI: 10.5220/0011888500003417

in Bibtex Style

@conference{visapp23,
author={Kirsty-Lee Walker and Tevin Moodley},
title={Handwriting Recognition in Down Syndrome Learners Using Deep Learning Methods},
booktitle={Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 4: VISAPP},
year={2023},
pages={936-943},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011888500003417},
isbn={978-989-758-634-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 4: VISAPP
TI - Handwriting Recognition in Down Syndrome Learners Using Deep Learning Methods
SN - 978-989-758-634-7
AU - Walker K.
AU - Moodley T.
PY - 2023
SP - 936
EP - 943
DO - 10.5220/0011888500003417
PB - SciTePress