  Handwriting disorder (termed dysgraphia) is a far from a singular problem as
nearly 8.6% of the population in France is considered dysgraphic. Moreover,
research highlights the fundamental importance to detect and remediate these
handwriting difficulties as soon as possible as they may affect a child's
entire life, undermining performance and self-confidence in a wide variety of
school activities. At the moment, the detection of handwriting difficulties is
performed through a standard test called BHK. This detection, performed by
therapists, is laborious because of its high cost and subjectivity. We present
a digital approach to identify and characterize handwriting difficulties via a
Recurrent Neural Network model (RNN). The child under investigation is asked to
write on a graphics tablet all the letters of the alphabet as well as the ten
digits. Once complete, the RNN delivers a diagnosis in a few milliseconds and
demonstrates remarkable efficiency as it correctly identifies more than 90% of
children diagnosed as dysgraphic using the BHK test. The main advantage of our
tablet-based system is that it captures the dynamic features of writing --
something a human expert, such as a teacher, is unable to do. We show that
incorporating the dynamic information available by the use of tablet is highly
beneficial to our digital test to discriminate between typically-developing and
dysgraphic children.
