As digitization in engineering progressed, circuit diagrams (also referred to
as schematics) are typically developed and maintained in computer-aided
engineering (CAE) systems, thus allowing for automated verification, simulation
and further processing in downstream engineering steps. However, apart from
printed legacy schematics, hand-drawn circuit diagrams are still used today in
the educational domain, where they serve as an easily accessible mean for
trainees and students to learn drawing this type of diagrams. Furthermore,
hand-drawn schematics are typically used in examinations due to legal
constraints. In order to harness the capabilities of digital circuit
representations, automated means for extracting the electrical graph from
raster graphics are required.
While respective approaches have been proposed in literature, they are
typically conducted on small or non-disclosed datasets. This paper describes a
modular end-to-end solution on a larger, public dataset, in which approaches
for the individual sub-tasks are evaluated to form a new baseline. These
sub-tasks include object detection (for electrical symbols and texts), binary
segmentation (drafter's stroke vs. background), handwritten character
recognition and orientation regression for electrical symbols and texts.
Furthermore, computer-vision graph assembly and rectification algorithms are
presented. All methods are integrated in a publicly available prototype.

通过在较大的公共数据集上评估各个子任务的方法，该论文描述了一种模块化的端到端解决方案，其中包括电气符号和文本的对象检测、二值分割、手写字符识别和方向回归，进而提供了电路图的自动化提取方法。