Using generative models for handwritten digit recognition
Michael Revow, Christopher K. I. Williams
and Geoffrey E. Hinton
Department of Computer Science
University of Toronto
We describe a method of recognizing handwritten digits by fitting
generative models that are built from deformable B-splines with Gaussian "ink
generators'' spaced along the length of the spline. The splines are adjusted using a novel
elastic matching procedure based on the Expectation Maximization (EM) algorithm that
maximizes the likelihood of the model generating the data. This approach has many
advantages. (1) After identifying the model most likely to have generated the data, the
system not only produces a classification of the digit but also a rich description of the
instantiation parameters which can yield information such as the writing style. (2) During
the process of explaining the image, generative models can perform recognition driven
segmentation. (3) The method involves a relatively small number of parameters and hence
training is relatively easy and fast. (4) Unlike many other recognition schemes it does
not rely on some form of pre-normalization of input images, but can handle arbitrary
scalings, translations and a limited degree of image rotation. We have demonstrated our
method of fitting models to images does not get trapped in poor local minima. The main
disadvantage of the method is it requires much more computation than more standard OCR
techniques.
Download: Postscript
IEEE Transactions on Pattern Analysis and Machine Intelligence,
18, 592-606.
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