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This question is related to a new hobby project i want to start. I have some experience with ML techniques and neural networks, although only for regression problems as of now.

In my classification problem i get an image of multiple game cards, like those of a trading card game. I want to identify which card is shown automatically.

I have thought of template matching and neural networks.

Template matching because the cards are exactly identical everytime, so i figured they are not hard to detect. But what changes is the angle and lighting of the pictures and i have heard template matching often fails for this.

Then i can use neural networks, which are very robust in those terms. I figure there are already some proven models existing, although something like AlexNet may be a bit too complex for my problem?

Is it advisable to use a neural network? If yes, what model should i use? Or is there a better machine learning technique that suits my needs?

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This is a very simple problem in terms of computer vision. I would recommend some processing to normalize the images and then trying something as simple nearest neighbors with mahalanobis distance.

A CNN will likely work well but comes with the need of massive data sets and computational power. As always in ML try the simple models before increasing your complexity.

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  • $\begingroup$ Thank you for the valuable suggestion. I have looked into the topic, but it seems i would need some pre-processing in order to identify the rough shape of the cards in an image before using nearest neighbor on every identified card? $\endgroup$ – JanM Sep 18 '17 at 6:13

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