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Proceedings Paper

Federal Register document image database
Author(s): Michael D. Garris; Stanley A. Janet; William W. Klein
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Paper Abstract

A new, fully-automated process has been developed at NIST to derive ground truth for document images. The method involves matching optical character recognition (OCR) results from a page with typesetting files for an entire book. Public domain software used to derive the ground truth is provided in the form of Perl scripts and C source code, and includes new, more efficient string alignment technology and a word- level scoring package. With this ground truthing technology, it is now feasible to produce much larger data sets, at much lower cost, than was ever possible with previous labor- intensive, manual data collection projects. Using this method, NIST has produced a new document image database for evaluating Document Analysis and Recognition technologies and Information Retrieval systems. The database produced contains scanned images, SGML-tagged ground truth text, commercial OCR results, and image quality assessment results for pages published in the 1994 Federal Register. These data files are useful in a wide variety of experiments and research. There were roughly 250 issues, comprised of nearly 69,000 pages, published in the Federal Register in 1994. This volume of the database contains the pages of 20 books published in January of that year. In all, there are 4711 page images provided, with 4519 of them having corresponding ground truth. This volume is distributed on two ISO-9660 CD- ROMs. Future volumes may be released, depending on the level of interest.

Paper Details

Date Published: 7 January 1999
PDF: 12 pages
Proc. SPIE 3651, Document Recognition and Retrieval VI, (7 January 1999); doi: 10.1117/12.335807
Show Author Affiliations
Michael D. Garris, National Institute of Standards and Technology (United States)
Stanley A. Janet, National Institute of Standards and Technology (United States)
William W. Klein, National Institute of Standards and Technology (United States)

Published in SPIE Proceedings Vol. 3651:
Document Recognition and Retrieval VI
Daniel P. Lopresti; Jiangying Zhou, Editor(s)

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