DyGAN: Generative Adversarial Network for Reproducing Handwriting Affected by Dyspraxia

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.