# How can a neural network distinguish a rotated 6 and 9 digits?

Rotated MNIST is a popular dataset for benchmarking models equivariant to rotations on $$\mathbb{R}^2$$, described by $$SO(2)$$ group or its discrete subgroups like $$\mathbb{Z}^{n}$$:

It consists of all digits from 0 to 9 rotated on an arbitrary angle from $$[0, 2 \pi)$$. However, what makes me a bit puzzled is that digits $$6$$ and $$9$$ seem to be confused by any learning algorithms, since from the view of human perception $$6$$ rotated by 180 degrees is equivalent to $$9$$ and vice versa.

The original paper in the description of Rotated MNIST doesn't comment on this point at all, which is strange, since it is a very natural question to ask.

In the paper Oriented Response Networks - authors plot embeddings of rotated digits projected via t-SNE on a 2d plane. There is a clear separation between all rotated versions of 6 and the rotated version of 9 for ORN.

I do not understand how it can be achieved? Probably, the networks understand much more in writing the digit, there are some subtle features, inaccessible to humans, but recognizable by powerful classifier?