  Learning disabilities like dysgraphia, dyslexia, dyspraxia, etc. interfere
with academic achievements but have also long terms consequences beyond the
academic time. It is widely admitted that between 5% to 10% of the world
population is subject to this kind of disabilities. For assessing such
disabilities in early childhood, children have to solve a battery of tests.
Human experts score these tests, and decide whether the children require
specific education strategy on the basis of their marks. The assessment can be
lengthy, costly and emotionally painful. In this paper, we investigate how
Artificial Intelligence can help in automating this assessment. Gathering a
dataset of handwritten text pictures and audio recordings, both from standard
children and from dyslexic and/or dysgraphic children, we apply machine
learning techniques for classification in order to analyze the differences
between dyslexic/dysgraphic and standard readers/writers and to build a model.
The model is trained on simple features obtained by analysing the pictures and
the audio files. Our preliminary implementation shows relatively high
performances on the dataset we have used. This suggests the possibility to
screen dyslexia and dysgraphia via non-invasive methods in an accurate way as
soon as enough data are available.
