<title>Classification and distribution of optical character recognition errors</title>

Abstract

This paper describes an approach for classifying OCR errors based on a new variation of a well-known dynamic programming algorithm. We present results from a large-scale experiment we performed involving the printing, scanning, and OCRing of over one million characters in each of three fonts. Times, Helvetica, and Courier. Our data allows us to draw a number of interesting conclusions about the nature of OCR errors for a particular font, as well as the relationship between error sets for different fonts.