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Beginners Question: scale on optical character recognition
  • I am currently doing Coursera's Machine Learning class. In an exercise on multi-class classification, you tried out both logistic regression and a neural network with a single hidden layer to do recognition of handwritten digits on 20x20 image. You had to "unroll" the image matrix to get a 400 input vector that you would input as features into these algorithms. The neural network performed somewhat better.

    However, I got a sense that the machine learned what a six was, for example, as long as it was drawn about the same size relative to the 20x20 pixel format. Given the same 20x20 pixel space, I could draw a smaller six in the upper-right 1/4th of the space and I dont know if the algorithm would be able to classify it because its input pixel-based features would be distributed in a very different way.

    Do you think the neural network would be able to recognise the smaller six? If not, how may you solve the problem of scale?

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