<p>QATM:Quality-Aware Template Matching For Deep Learning might be what you are looking for.</p>

<p>Original paper : <a href="https://arxiv.org/abs/1903.07254" rel="nofollow noreferrer">https://arxiv.org/abs/1903.07254</a></p>

<p>And the following github contain an example with electric scheme:
<a href="https://github.com/kamata1729/QATM_pytorch" rel="nofollow noreferrer">https://github.com/kamata1729/QATM_pytorch</a></p>

<p><a href="https://i.stack.imgur.com/1Ucvz.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/1Ucvz.png" alt="enter image description here"></a></p>
Symbol detection in scheme using Computer Vision
<p>I am trying to detect electrical symbol in electrical scheme.
Here I think 2 ways could be use:</p>

<ul>
<li>classical way with OpenCV, I tried re to recognise shape with opencv and python but some symbole are too complexe</li>
<li>deep learning way: I tried with Mask-RCNN using a handmade dataset of symbol but nothing get really successful</li>
</ul>

<p>Here is a really simple example of what I would like to do:
<a href="https://i.stack.imgur.com/kzpmt.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/kzpmt.png" alt="enter image description here"></a> </p>

<p>I think it could be easy to make a dataset of symbol but all symbol would be the same form and context of the image would not be represented.</p>

<p>How do you think I could handle this problem ? </p>