However, what I did was a bit different -- instead of looking for a crackle, I surmised that that 'crackling' effect actually is just of course slices of the data over different rifts in the parchment, and that the data of the ink lay on the manifold of that crackling and bending.
It would not be as clear to the human eye for all of the letters, I think, as there are many, many, many layers in the scanned image, and you can only start to see a pattern emerge over time as you cycle through the images.
I was working on code that minimized an optimization function that was basically the total variance loss if I recall correctly, where it just interpolated each pixel column up and down bilinearly to 'align' the blocks of the image so that the crackle texture was flattened.
From there I planned on using a rather optimized convolutional network on the 'flattened' image, which can I think be done rather efficiently as if you look at a cross section of the scroll you can see where it's like a tree in that the pinching and such seems to be somewhat locally consistent, so you might be able to get away with some interpolation.
I should probably share the code if this is of interest to anyone, since I'm not pursuing the competition at the moment.
Also, this is why I did not buy into 3D convolutions for this, at least. Ink that has been laid and dried should follow a semi-predictable pattern that a 2D convolution can detect, I do not know if a 3D convolution really brings us anything, as the invariances we desire can be structured up front more easily.
If there is interest in the code, let me know and I can do a little digging, otherwise, it is a fun challenge, for sure.