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Proceedings Paper

N-gram language models for document image decoding
Author(s): Gary E. Kopec; Maya R. Said; Kris Popat
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Paper Abstract

This paper explores the problem of incorporating linguistic constraints into document image decoding, a communication theory approach to document recognition. Probabilistic character n-grams (n=2--5) are used in a two-pass strategy where the decoder first uses a very weak language model to generate a lattice of candidate output strings. These are then re-scored in the second pass using the full language model. Experimental results based on both synthesized and scanned data show that this approach is capable of improving the error rate by a factor of two to ten depending on the quality of the data and the details of the language model used.

Paper Details

Date Published: 18 December 2001
PDF: 12 pages
Proc. SPIE 4670, Document Recognition and Retrieval IX, (18 December 2001); doi: 10.1117/12.450728
Show Author Affiliations
Gary E. Kopec, Xerox Palo Alto Research Ctr. (United States)
Maya R. Said, Massachusetts Institute of Technology (United States)
Kris Popat, Xerox Palo Alto Research Ctr. (United States)

Published in SPIE Proceedings Vol. 4670:
Document Recognition and Retrieval IX
Paul B. Kantor; Tapas Kanungo; Jiangying Zhou, Editor(s)

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