# A Public Ground-Truth Dataset for Handwritten Circuit Diagram Images

arXiv: https://arxiv.org/abs/2107.10373 · HTML: https://mlsanity.com/paper/2107.10373/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 13 references that are in the map.
- First posted on arXiv 21 Jul 2021.

## Builds on (most cited first, 2 of 2)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)

## Built on by (most cited first, 3 of 3)

- [Instance Segmentation Based Graph Extraction for Handwritten Circuit Diagram Images](https://mlsanity.com/paper/2301.03155/) (2023)
- [OmniSch: A Multimodal PCB Schematic Benchmark For Structured Diagram Visual Reasoning](https://mlsanity.com/paper/2604.00270/) (2026)
- [Modular Graph Extraction for Handwritten Circuit Diagram Images](https://mlsanity.com/paper/2402.11093/) (2024)

## Abstract (arXiv)

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.