Fast Lexicon-Based Scene Text Recognition with Sparse Belief Propagation

  • Authors:

  • J. Weinman;E. Learned-Miller;A. Hanson

  • Affiliations:

  • University of Massachusetts, Amherst, MA;University of Massachusetts, Amherst, MA;University of Massachusetts, Amherst, MA

  • Venue:
  • ICDAR '07 Proceedings of the Ninth International Conference on Document Analysis and Recognition - Volume 02
  • Year:
  • 2007

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Abstract

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 prop- erties, 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 hy- pothesis pruning. We give experimental results on recogniz- ing text from images of signs in outdoor scenes. Incorpo- rating the lexicon reduces word recognition error by 42% and sparse belief propagation reduces the number of lexi- con words considered by 97%.