Not really. The algorithms described in the article are pretty worthless (edge, blob detector) until the very last step, where they use the width / height ratio to filter out all of the noise and get the license plate out. As the parent comment said, this doesn’t work in the real world because of transalation/rotation. You can spend ages tweaking parameters manually, but the CNN will beat you - actually we have discovered that two DL models work best, one to locate the license plate, then the other to OCR (the former is faster computationally)
The methods described in the article are dated, and worthless
Disclaimer: I build number plate recognition systems
Maybe not exactly those techniques but I've heard of non-ML techniques being used in real life situations incl. translation/rotation to identify the plate (not read the numbers, just identify the plates)
Edit: They are dated and might have made sense some 8 years ago which is when I heard of them being used
While not ideal for the described scenario, if you are say scanning from a pool of limited form templates, it could be very useful and faster than ML methods, at least for an earlier pass. But this is a 2D scenario even then.
Marginally faster.... maybe. A forward pass on our two neural nets is less than 1.5 seconds. Is embarrassingly parallel, and has a much higher accuracy.
In this task accuracy is much much more important than computational efficiency, and given a forward pass is already quick (less than 2 seconds) why wouldn’t you use the superior method that always works?
The methods described in the article are dated, and worthless
Disclaimer: I build number plate recognition systems