![](bg5.png)
Figure 11: Mask Predictions on Validation Set (junctions
and texts omitted).
5 CONCLUSIONS
The semantic and instance segmentation additons to
the CGHD dataset establish a new, flexible standard
for further research on graph extraction from hand-
drawn schematics, allowing for arbitrary new meth-
ods to be evaluated.
While the experiment demonstrated the general
viability of the approach, further model optimizations
are required to achieve error-free graph reconstruc-
tion.
6 FUTURE WORK
So far, the described approach is limited in various
ways: Most importantly, the types of individual com-
ponent connectors are not differentiated, which is crit-
ical for an accurate simulation of non-linear electri-
cal circuits. Adding a rotation prediction head to the
Mask RCNN as well as providing these information in
the dataset (which can in turn be semi-automated by a
classic template matching) can be used in conjunction
with a component library to identify these connector
types. Furthermore, drawing errors like discontinu-
ous wires need to be mitigated, which could be done
by post-processing with graph neural networks. Apart
from that, OCR information need to be incorporated
to predict not only the position but also the content of
the text labels. Additionally, edge types different from
electrical connections need to be identified like me-
chanical coupling of switches or inductive coil cou-
pling in complex transformers. Finally, as mask in-
formation could only be provided for a subset of the
original dataset, a join training with full dataset on
both masks and bounding boxes only need to be con-
sidered.
ACKNOWLEDGEMENTS
The authors cordially tank all drafters and annotators
for contributing to the dataset. The reseach for this
paper was partly funded by the BMWE (Bundesmin-
isterium f
¨
ur Wirtschaft und Klimaschutz), project
ecoKI, funding number: 03EN2047B.
REFERENCES
Bailey, D., Norman, A., Moretti, G., and North, P. (1995).
Electronic schematic recognition. Massey University,
Wellington, New Zealand.
Dai, Y.-Y. and Braytont, R. K. (2017). Circuit recogni-
tion with deep learning. In 2017 IEEE International
Symposium on Hardware Oriented Security and Trust
(HOST), pages 162–162. IEEE.
Doll
´
ar, K. H. G. G. P. and Girshick, R. (2017). Mask r-cnn.
In Proceedings of the IEEE international conference
on computer vision, pages 2961–2969.
Lakshman Naika, R., Dinesh, R., and Prabhanjan, S.
(2019). Handwritten electric circuit diagram recog-
nition: an approach based on finite state machine. Int
J Mach Learn Comput, 9:374–380.
MAJEED, M. A., Almousa, T., Alsalman, M., and YOSEF,
A. (2020). Sketic: a machine learning-based dig-
ital circuit recognition platform. Turkish Journal
of Electrical Engineering and Computer Sciences,
28(4):2030–2045.
Mani, S., Haddad, M. A., Constantini, D., Douhard, W., Li,
Q., and Poirier, L. (2020). Automatic digitization of
engineering diagrams using deep learning and graph
search. In Proceedings of the IEEE/CVF Conference
on Computer Vision and Pattern Recognition Work-
shops, pages 176–177.
Nurminen, J. K., Rainio, K., Numminen, J.-P., Syrj
¨
anen, T.,
Paganus, N., and Honkoila, K. (2019). Object detec-
tion in design diagrams with machine learning. In In-
ternational Conference on Computer Recognition Sys-
tems, pages 27–36. Springer.
Peter Mattis, S. K. (1998-2022). GNU IMAGE MANIPU-
LATION PROGRAM. https://www.gimp.org/. [On-
line; accessed 16-November-2022].
Rabbani, M., Khoshkangini, R., Nagendraswamy, H., and
Conti, M. (2016). Hand drawn optical circuit recogni-
tion. Procedia Computer Science, 84:41–48.
Rahul, R., Paliwal, S., Sharma, M., and Vig, L.
(2019). Automatic information extraction from pip-
ing and instrumentation diagrams. arXiv preprint
arXiv:1901.11383.
Reddy, R. R. and Panicker, M. R. (2021). Hand-drawn elec-
trical circuit recognition using object detection and
node recognition. arXiv preprint arXiv:2106.11559.
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