Using a Probabilistic Syllable Model to Improve Scene Text Recognition

This paper presents a new language model for text recognition in natural images. Many existing techniques incorporate n-gram information as an additional source of information. One problem is that some n-grams are very uncommon, but will still appear in a word across a syllable boundary. These words are given a low probability under an n-gram model. To overcome this problem, we introduce a probabilistic syllable model that uses a probabilistic context-free grammar to generate recognized word labels that are consistent with syllables. In other words, labels generated by this model are pronounceable. This is important for scene text recognition where text often includes proper nouns and standard dictionary information cannot be a useful resource. We show that this language model leads to increased recognition accuracy over a big ram model and discuss the benefits over a dictionary model.