The doi above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
doi: 10.4121/ad04db6b-cf81-438d-9506-80ba8184bb10

Bayer, Johannes (2024): A Public Ground-Truth Dataset for Handwritten Circuit Diagram Images. Version 1. 4TU.ResearchData. software. https://doi.org/10.4121/ad04db6b-cf81-438d-9506-80ba8184bb10.v1

Software

• Artificial Intelligence and Image Processing
• Electrical and Electronic Engineering
• Engineering
• Information and Computing Sciences

CGHD, Circuit Diagram, CircuitGraph, Handwriting Recognition, Object Recognition, Schematic, Semantic Segmentation

CC BY-SA 4.0

RefWorks, BibTeX, Reference Manager, Endnote, DataCite, NLM, DC, CFF

by Johannes Bayer

CGHD

This dataset contains images of hand-drawn electrical circuit diagrams as well as accompanying annotation and segmentation ground-truth files. It is intended to train (e.g. ANN) models for extracting electrical graphs from raster graphics.

Content

• 2.549 Raw Images (Annotated)
• 25 Drafters (plus Images provided by TU Dresden)
• 12 Circuits per Drafter
• 2 Drawings per Circuit
• 4 Photos per Drawing
• 212.280 Bounding Box Annotations
• 29.790 Rotation Annotations
• 71.307 Text String Annotations
• 264 Binary Segmentation Maps (Annotated)
• Strokes vs. Background
• Accompanying Polygon Annotation Files
• 20.060 Polygon Annotations
• 59 Object Classes
• Scripts for Data Loading, Statistics, Consistency Check and Training Preparation

• 2024-01-23 first online, published, posted

German Research Center for Artificial Intelligence

image/jpeg

A Public Ground-Truth Dataset for Handwritten Circuit Diagram Images

• https://gitlab.com/circuitgraph

• https://zenodo.org/records/10056817

Smart Data and Knowledge Services Department, DFKI GmbH, Kaiserslautern, Germany
Computer Science Department, TU Kaiserslautern, Germany