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Proceedings Paper

Classification and distribution of optical character recognition errors
Author(s): Jeffrey Esakov; Daniel P. Lopresti; Jonathan S. Sandberg
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Paper 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.

Paper Details

Date Published: 23 March 1994
PDF: 13 pages
Proc. SPIE 2181, Document Recognition, (23 March 1994); doi: 10.1117/12.171108
Show Author Affiliations
Jeffrey Esakov, Matsushita Information Technology Lab. (United States)
Daniel P. Lopresti, Matsushita Information Technology Lab. (United States)
Jonathan S. Sandberg, Matsushita Information Technology Lab. (United States)


Published in SPIE Proceedings Vol. 2181:
Document Recognition
Luc M. Vincent; Theo Pavlidis, Editor(s)

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