Automated Card Grading - Surface

[Case12]

Curious - are you using TensorFlow?

[kevinlenane]

To answer questions on the surface details - anything that the human eye can detect a machine can as well (w greater resolution on most new cameras) so any edge cases can be accounted for by simply accounting for them with real and synthetic data. It's worth noting that it is expected that there will quite a bit of this kind of give and take from user to company - feedback is going to be welcome and utilized so something like the nuances of Hocus focus or T card edge cuts would require some feedback and then review for accuracy/ethics before it gets accommodated in the grading. Generally you can expect the data to be extremely transparent and eventually explanatory.

[kevinlenane]

FWIW I haven't seen a surface crease yet that can't be detected at a pixel level - but that would corroborate with numbers from physical card a training data meaning the human can inspect it in anyway to provide the grades associated w the image. In this way - there are no gaps between eyes and lens. The only thing that might get missed in this dual training model is a stain or
mark that only exists on the edge of the card but doesn't show on the front or back when looking at the edges. I've only been able to produce this on extremely thick cards like the modern memorabilia/insert stuff - but in okay not worrying to much about that case right now...