  We describe a purely image-based method for finding geometric constructions
with a ruler and compass in the Euclidea geometric game. The method is based on
adapting the Mask R-CNN state-of-the-art image processing neural architecture
and adding a tree-based search procedure to it. In a supervised setting, the
method learns to solve all 68 kinds of geometric construction problems from the
first six level packs of Euclidea with an average 92% accuracy. When evaluated
on new kinds of problems, the method can solve 31 of the 68 kinds of Euclidea
problems. We believe that this is the first time that a purely image-based
learning has been trained to solve geometric construction problems of this
difficulty.
