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2. Classification and distribution of optical character recognition errors

Author

Jonathan Sandberg, Jeffrey Esakov, and Daniel P. Lopresti

Subjects
business.industry, Computer science, Speech recognition, Computer programming, Pattern recognition, Optical character recognition, Variation (game tree), computer.software_genre, Dynamic programming, Distribution (mathematics), Font, ComputingMethodologies_DOCUMENTANDTEXTPROCESSING, Artificial intelligence, business, computer
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.

Published
1994
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