![]() I decided to use Google's TensorFlow machine learning framework to train a playing card detection classifier. Object detection classifiers recognize patterns to identify objects, so they only need to see a portion of the object to detect it. Someone told me that I might be able to train an object detection classifier (a type of neural network) to recognize the cards even if they're partially obscured or overlapping. If my blackjack robot is going to work, it needs to be able to count cards even when they're overlapping. ![]() Unfortunately, blackjack is always dealt with the cards overlapping. The OpenCV algorithm I used (described in this video) works great at detecting cards, but it doesn't work if the cards are overlapping even the slightest bit. ![]() Over the past couple months, I've been tinkering with machine learning to try and train an object detection neural network that can detect playing cards. ![]()
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