NIPS preprint in neuroprose

[Geoffrey Hinton]

The following paper is available as hinton.handwriting.ps.Z in neuroprose


ADAPTIVE ELASTIC MODELS FOR HAND-PRINTED CHARACTER RECOGNITION

Geoffrey E. Hinton, Christopher K. I. Williams and Michael D. Revow
Department of Computer Science, University of Toronto

 				ABSTRACT

Hand-printed digits can be modeled as splines that are governed
by about 8 control points.  For each known digit, the control
points have preferred "home" locations, and deformations of the
digit are generated by moving the control points away from their
home locations.  Images of digits can be produced by placing
Gaussian ink generators uniformly along the spline.  Real images
can be recognized by finding the digit model most likely to have
generated the data.  For each digit model we use an elastic
matching algorithm to minimize an energy function that includes
both the deformation energy of the digit model and the log
probability that the model would generate the inked pixels in the
image. The model with the lowest total energy wins.  If a uniform
noise process is included in the model of image generation, some
of the inked pixels can be rejected as noise as a digit model is
fitting a poorly segmented image.  The digit models learn by
modifying the home locations of the control points.