Share Email Print

Proceedings Paper

Adding linguistic constraints to document image decoding: comparing the iterated complete path and stack algorithms
Author(s): Kris Popat; Daniel H. Greene; Justin K. Romberg; Dan S. Bloomberg
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

Beginning with an observed document image and a model of how the image has been degraded, Document Image Decoding recognizes printed text by attempting to find a most probable path through a hypothesized Markov source. The incorporation of linguistic constraints, which are expressed by a sequential predictive probabilistic language model, can improve recognition accuracy significantly in the case of moderately to severely corrupted documents. Two methods of incorporating linguistic constraints in the best-path search are described, analyzed and compared. The first, called the iterated complete path algorithm, involves iteratively rescoring complete paths using conditional language model probability distributions of increasing order, expanding state only as necessary with each iteration. A property of this approach is that it results in a solution that is exactly optimal with respect to the specified source, degradation, and language models; no approximation is necessary. The second approach considered is the Stack algorithm, which is often used in speech recognition and in the decoding of convolutional codes. Experimental results are presented in which text line images that have been corrupted in a known way are recognized using both the ICP and Stack algorithms. This controlled experimental setting preserves many of the essential features and challenges of real text line decoding, while highlighting the important algorithmic issues.

Paper Details

Date Published: 21 December 2000
PDF: 13 pages
Proc. SPIE 4307, Document Recognition and Retrieval VIII, (21 December 2000); doi: 10.1117/12.410844
Show Author Affiliations
Kris Popat, Xerox Palo Alto Research Ctr. (United States)
Daniel H. Greene, Xerox Palo Alto Research Ctr. (United States)
Justin K. Romberg, Rice Univ. (United States)
Dan S. Bloomberg, Xerox Palo Alto Research Ctr. (United States)

Published in SPIE Proceedings Vol. 4307:
Document Recognition and Retrieval VIII
Paul B. Kantor; Daniel P. Lopresti; Jiangying Zhou, Editor(s)

© SPIE. Terms of Use
Back to Top