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.

翻译：从教育情景或历史来源获得的手写电路图通常存在于模拟介质上。为了自动得出功能原理或缺陷,它们需要数字化,提取电子图。最近,促进这一过程的自动管道的基础技术从计算机视野转向机器学习。本文描述了一种方法,通过例分解和关键点提取手段提取电气部件(包括其终端和描述文字)及其互联(包括连接点和电线跳),由此产生的图形提取过程包括一个简单的两步过程,即模型推论过程和微不足道的几何关键点匹配。数据集本身、其编制、模型培训和后处理都作了说明并公开提供。