  Handwritten circuit diagrams from educational scenarios or historic sources
usually exist on analogue media. For deriving their functional principles or
flaws automatically, they need to be digitized, extracting their electrical
graph. Recently, the base technologies for automated pipelines facilitating
this process shifted from computer vision to machine learning. This paper
describes an approach for extracting both the electrical components (including
their terminals and describing texts) as well their interconnections (including
junctions and wire hops) by the means of instance segmentation and keypoint
extraction. Consequently, the resulting graph extraction process consists of a
simple two-step process of model inference and trivial geometric keypoint
matching. The dataset itself, its preparation, model training and
post-processing are described and publicly available.
