  The development of digitization methods for line drawings (especially in the
area of electrical engineering) relies on the availability of publicly
available training and evaluation data. This paper presents such an image set
along with annotations. The dataset consists of 1152 images of 144 circuits by
12 drafters and 48 563 annotations. Each of these images depicts an electrical
circuit diagram, taken by consumer grade cameras under varying lighting
conditions and perspectives. A variety of different pencil types and surface
materials has been used. For each image, all individual electrical components
are annotated with bounding boxes and one out of 45 class labels. In order to
simplify a graph extraction process, different helper symbols like junction
points and crossovers are introduced, while texts are annotated as well. The
geometric and taxonomic problems arising from this task as well as the classes
themselves and statistics of their appearances are stated. The performance of a
standard Faster RCNN on the dataset is provided as an object detection
baseline.
