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Handwritten circuit diagrams from educational scenarios or historic sourcesusually exist on analogue media. For deriving their functional principles orflaws automatically, they need to be digitized, extracting their electricalgraph. Recently, the base technologies for automated pipelines facilitatingthis process shifted from computer vision to machine learning. This paperdescribes an approach for extracting both the electrical components (includingtheir terminals and describing texts) as well their interconnections (includingjunctions and wire hops) by the means of instance segmentation and keypointextraction. Consequently, the resulting graph extraction process consists of asimple two-step process of model inference and trivial geometric keypointmatching. The dataset itself, its preparation, model training andpost-processing are described and publicly available.

Instance Segmentation Based Graph Extraction for Handwritten Circuit Diagram Images