Dyslexia is developed by neurobiological in origin which is categorized as
learning disorder that affect the ability to read, spell, write and speak. The
most common dyslexia symptom can easily be identified through the
handwriting pattern. There are many intelligence and computational
methods that have been proposed, and they have provided various and
different performance to evaluate the proposed system ability. However,
system performances are varied and nonstandardized in each assesment on
dyslexic children to validate the presence of dyslexia symptom. The recent
deep learning models have been employed to improve the assesment
performance and (the models/ they have shown) shows significant output to
detect and classify the present of dyslexia symptoms among school children.
Therefore, there is a crucial need in deep learning, specifically for
Convolutional Neural Network ( CNN) to validate performances of different
networks, so that the most performed CNN could be a bench mark in
evaluation to detect such symptom. This study aims to compare different deep
learning networks specifically the CNN models to validate its performance
in terms of the capability to classify dyslexic handwriting among school
children. This study is proposed to compare different CNN models such as
CNN-1, CNN-2, CNN-3 and LeNet-5. The proposed methods to compare the
CNN performances are developed by using Jupyter notebook as platform.
Meanwhile, keras is the higher-level API framework to provide a more
flexible way for defining models. It specifically allows to define multiple
input or output models as well as models that share layers. The tensorflow is
also used for machine learning applications such as neural networks. Before
that, the dataset of handwriting image is preprocessed by the augmentation
process which includes the rotation of all images. CNN models have shown
significant performance and provided sufficient results of performance with
more than 87% of accuracy in classifying the potential dyslexia symptom
based on handwritten images.