(Feel free to suggest a better title) (same with tags, none of relevant tags exist yet, and I only have 101)
Let's say I have a perceptron with an optimal number of layers and optimal number of neurons, which I have taught to recognize an alphabet which was printed in a certain font, with the same settings and resolution. Now if I feed it a symbol from that set, but say, printed in another (but similar) font, or with the same font, but rotated 5 degrees, or shifted 2 pixels to the left, or scaled a 5 percent, and so on, will this system recognize this modified symbol, or at least have an easier time learning to recognize this symbol? (than a system without any prior learning)
Basically, what is the point of back-propagating neural networks? Is it 1) a way to recognize shapes, and effectively filter noise in images of those shapes, in form of different rotations, scales and other disortions or 2) just a relatively simple and fast way to recognize a limited number of bitmaps of those shapes, and not being able to recognize the shape in another bitmap if that particular bitmap was never taught to the network.