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%.