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

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 correcti...

• Type: Conference
• Year: 2008
• Where: DRR
• Authors: Pingping Xiu, Henry S. Baird
• Keywords: Automatic Mutual-entropy-based Model, Document Analysis, DRR 2008, Image Classification, Posteriori Probabilities