Text Detection on Technical Drawings for the Digitization of Brown-field Processes
This addresses the challenge of digitizing brown-field processes in manufacturing, where paper-based technical drawings are common but lack automated reading solutions, though it is incremental as it builds on existing object detection methods with generated data.
The paper tackles the problem of autonomously detecting text on technical drawings, which is unreliable with existing methods due to limited real data and complex layouts, by combining a domain knowledge-based generator to create artificial drawings with a state-of-the-art object detection model, showing that artificially generated data improves detection quality as the number of drawings increases.
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.