  Dysgraphia is a neurodevelopmental condition in which children encounter
difficulties in handwriting. Dysgraphia is not a disorder per se, but is
secondary to neurodevelopmental disorders, mainly dyslexia, Developmental
Coordination Disorder (DCD, also known as dyspraxia) or Attention Deficit
Hyperactivity Disorder (ADHD). Since the mastering of handwriting is central
for the further acquisition of other skills such as orthograph or syntax, an
early diagnosis and handling of dysgraphia is thus essential for the academic
success of children. In this paper, we investigated a large handwriting
database composed of 36 individual symbols (26 isolated letters of the Latin
alphabet written in cursive and the 10 digits) written by 545 children from 6,5
to 16 years old, among which 66 displayed dysgraphia (around 12\%). To better
understand the dynamics of handwriting, mathematical models of nonpathological
handwriting have been proposed, assuming oscillatory and fluid generation of
strokes (Parsimonious Oscillatory Model of Handwriting [Andr\'e, 2014]). The
purpose of this work is to study how such models behave when applied to
children dysgraphic handwriting, and whether a lack of fit may help in the
diagnosis, using a two-layer classification procedure with different
compositions of classification algorithms.
