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Proceedings Paper

Whole-book recognition using mutual-entropy-driven model adaptation
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Paper Abstract

We describe an approach to unsupervised high-accuracy recognition of the textual contents of an entire book using fully automatic mutual-entropy-based model adaptation. Given images of all the pages of a book together with approximate models of image formation (e.g. a character-image classifier) and linguistics (e.g. a word-occurrence probability model), we detect evidence for disagreements between the two models by analyzing the mutual entropy between two kinds of probability distributions: (1) the a posteriori probabilities of character classes (the recognition results from image classification alone), and (2) the a posteriori probabilities of word classes (the recognition results from image classification combined with linguistic constraints). The most serious of these disagreements are identified as candidates for automatic corrections to one or the other of the models. We describe a formal information-theoretic framework for detecting model disagreement and for proposing corrections. We illustrate this approach on a small test case selected from real book-image data. This reveals that a sequence of automatic model corrections can drive improvements in both models, and can achieve a lower recognition error rate. The importance of considering the contents of the whole book is motivated by a series of studies, over the last decade, showing that isogeny can be exploited to achieve unsupervised improvements in recognition accuracy.

Paper Details

Date Published: 28 January 2008
PDF: 10 pages
Proc. SPIE 6815, Document Recognition and Retrieval XV, 681506 (28 January 2008); doi: 10.1117/12.767121
Show Author Affiliations
Pingping Xiu, Lehigh Univ. (United States)
Henry S. Baird, Lehigh Univ. (United States)

Published in SPIE Proceedings Vol. 6815:
Document Recognition and Retrieval XV
Berrin A. Yanikoglu; Kathrin Berkner, Editor(s)

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