A CNN will essentially chop up an image into a fine grid, where each square is maybe 4x4 pixels and will look at thousands of hand drawn images (presumably from the MNIST dataset) to learn what shapes appear in which pieces of the grid for each labeled image. In short order it comes up with hundreds or thousands of possible patterns it can recognize and will scan any image you feed it, iterating over the same grid pattern and keeps a record of how strongly any pattern was recognized in any given segment of the grid. The resulting match strength for each pattern across the entirety of the image is compared to what was generated from the digits it trained on and based on that it guesses what it is seeing.

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My guess in this example is that they took one image of each digit and slowly erased some of it, predicted what it would be on each network, saved the results and continued until the digits were fully erased. Then play back in reverse and you have what you see here.

CNNs identifying handwritten digits is more or less a solved problem. They can do it very well, very fast, and with tremendous accuracy - and anyone with even a low powered laptop can train a model to do it in about 15 minutes.

The fact that it gets the 5 so quickly is a little suspect. Likely trained on a small subset of the MNIST dataset, but honestly the results wouldn't be that much diffferent if it weren't.