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ICDAR
2007
IEEE

Fast Lexicon-Based Scene Text Recognition with Sparse Belief Propagation

9 years 6 months ago
Fast Lexicon-Based Scene Text Recognition with Sparse Belief Propagation
Using a lexicon can often improve character recognition under challenging conditions, such as poor image quality or unusual fonts. We propose a flexible probabilistic model for character recognition that integrates local language properties, such as bigrams, with lexical decision, having open and closed vocabulary modes that operate simultaneously. Lexical processing is accelerated by performing inference with sparse belief propagation, a bottom-up method for hypothesis pruning. We give experimental results on recognizing text from images of signs in outdoor scenes. Incorporating the lexicon reduces word recognition error by 42% and sparse belief propagation reduces the number of lexicon words considered by 97%.
Jerod J. Weinman, Erik G. Learned-Miller, Allen R.
Added 03 Jun 2010
Updated 03 Jun 2010
Type Conference
Year 2007
Where ICDAR
Authors Jerod J. Weinman, Erik G. Learned-Miller, Allen R. Hanson
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