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DOI: 10.14569/IJACSA.2025.0160222
PDF

DyGAN: Generative Adversarial Network for Reproducing Handwriting Affected by Dyspraxia

Author 1: Jes´us Jaime Moreno Escobar
Author 2: Hugo Quintana Espinosa
Author 3: Erika Yolanda Aguilar del Villar

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 16 Issue 2, 2025.

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Abstract: Dyspraxia primarily affects coordination and is categorized into two forms: 1) Motor, and 2) Verbal ororal. This study focuses on motor dyspraxia, which influences individuals in learning movement-related tasks. Consequently, the DyGAN initiative employs deep convolutional aversarial generation networks, using deep learning to create characters resembling human handwriting. The methodology in this study is structured into two main stages: 1) the creation of a first-order cybernetic model, and 2) the execution phase. Using four independent variables and three dependent variables, eight outcomes were analyzed using variance analysis. DyGAN is a twin Deep Convolutional Neural Networks and it is highly sensitive to the Learning Rate. It scored a 67% on the proposal, suggesting that characters can sound written by a human. The project will feature writers from different backgrounds and will help augment data for writing resources for dyspraxia, potentially benefiting those struggling with writing difficulties and improving our understanding of education. The model is designed to be widely applicable. Future work could customize the model to mimic the way a specific child writes, with neural networks, for example.

Keywords: Children with neurodevelopmental disorders; dyspraxia; generative adversarial network; deep learning; deep convo-lutional neural network; human handwriting

Jes´us Jaime Moreno Escobar, Hugo Quintana Espinosa and Erika Yolanda Aguilar del Villar, “DyGAN: Generative Adversarial Network for Reproducing Handwriting Affected by Dyspraxia” International Journal of Advanced Computer Science and Applications(IJACSA), 16(2), 2025. http://dx.doi.org/10.14569/IJACSA.2025.0160222

@article{Escobar2025,
title = {DyGAN: Generative Adversarial Network for Reproducing Handwriting Affected by Dyspraxia},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0160222},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0160222},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {2},
author = {Jes´us Jaime Moreno Escobar and Hugo Quintana Espinosa and Erika Yolanda Aguilar del Villar}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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