Modular Graph Extraction for Handwritten Circuit Diagram Images

• URL: http://arxiv.org/abs/2402.11093v1
• Date: Fri, 16 Feb 2024 21:39:28 GMT
• Title: Modular Graph Extraction for Handwritten Circuit Diagram Images
• Authors: Johannes Bayer, Leo van Waveren, Andreas Dengel
• Abstract summary: 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. In order to harness the capabilities of digital circuit representations, automated means for extracting the electrical graph from graphics are required. 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.
• Score: 5.780326596446099
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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