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Handwriting disorder (termed dysgraphia) is a far from a singular problem asnearly 8.6% of the population in France is considered dysgraphic. Moreover,research highlights the fundamental importance to detect and remediate thesehandwriting difficulties as soon as possible as they may affect a child'sentire life, undermining performance and self-confidence in a wide variety ofschool activities. At the moment, the detection of handwriting difficulties isperformed through a standard test called BHK. This detection, performed bytherapists, is laborious because of its high cost and subjectivity. We presenta digital approach to identify and characterize handwriting difficulties via aRecurrent Neural Network model (RNN). The child under investigation is asked towrite on a graphics tablet all the letters of the alphabet as well as the tendigits. Once complete, the RNN delivers a diagnosis in a few milliseconds anddemonstrates remarkable efficiency as it correctly identifies more than 90% ofchildren diagnosed as dysgraphic using the BHK test. The main advantage of ourtablet-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 thatincorporating the dynamic information available by the use of tablet is highlybeneficial to our digital test to discriminate between typically-developing anddysgraphic children.

The Dynamics of Handwriting Improves the Automated Diagnosis of Dysgraphia