  This paper addresses the issue of autonomously detecting text on technical
drawings. The detection of text on technical drawings is a critical step
towards autonomous production machines, especially for brown-field processes,
where no closed CAD-CAM solutions are available yet. Automating the process of
reading and detecting text on technical drawings reduces the effort for
handling inefficient media interruptions due to paper-based processes, which
are often todays quasi-standard in brown-field processes. However, there are no
reliable methods available yet to solve the issue of automatically detecting
text on technical drawings. The unreliable detection of the contents on
technical drawings using classical detection and object character recognition
(OCR) tools is mainly due to the limited number of technical drawings and the
captcha-like structure of the contents. Text is often combined with unknown
symbols and interruptions by lines. Additionally, due to intellectual property
rights and technical know-how issues, there are no out-of-the box training
datasets available in the literature to train such models. This paper combines
a domain knowledge-based generator to generate realistic technical drawings
with a state-of-the-art object detection model to solve the issue of detecting
text on technical drawings. The generator yields artificial technical drawings
in a large variety and can be considered as a data augmentation generator.
These artificial drawings are used for training, while the model is tested on
real data. The authors show that artificially generated data of technical
drawings improve the detection quality with an increasing number of drawings.
