Handwriting Recognition in Down Syndrome Learners Using Deep Learning Methods

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.