| Summary: | Assessing the acquisition of pre-writing skills in children with and without special educational needs is a time-consuming task for educators and therapists. It also involves a level of subjectivity, because the same set of strokes may receive different scores from different professionals. We present a system that automates the task by rating the execution of elementary figures (circle, square and triangle) according to the criteria of the Battelle guide for fine motor skills rating. The system uses a neural network trained with a collection of images drawn by 300 children and optimized through a systematic scan of hyperparameters, which revealed that shape signatures are better descriptors than Hu moments. Experiments carried out in collaboration with educators and therapists in Cuenca (Ecuador) provide evidence that the proposed system facilitates their work, automatically providing reliable assessments and in much shorter time than they would need for manual assessment, thus freeing their valuable time for education and therapy tasks.
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