#### A Statistical Model for Computer Recognition of Handwritten ZIP Codes

Steve C. Wang

Computing Science and Statistics 31, pp. 221-225 (1999)

I present a statistical model for computer recognition of human
handwriting, specifically ZIP codes. I incorporate Bayesian principles to build a
model for integrating two major tasks in handwriting recognition: the
segmentation of a sequence of characters into its individual components, and the
recognition of these individual components.

The model describes how to use the information extracted from a ZIP code image to
update our prior knowledge about a candidate segmentation. I incorporate a digit
classification algorithm developed by Amit, Geman, and Wilder to recognize the
characters determined by the candidate segmentation. The strength of this
recognition provides additional information about the plausibility of the
segmentation.

Combining these sources of information, we obtain a posterior distribution that
simultaneously optimizes both segmentation and recognition. Summing this
posterior distribution over all segmentations gives us a posterior distribution on the
recognition alone, and we take its mode as our best prediction of the true ZIP code.
To make this optimization feasible, a generalized dynamic programming
algorithm is implemented.

Return to Steve's home page.