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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 worddoi:10.1109/icdar.2013.183 dblp:conf/icdar/FeildLS13 fatcat:clmz3y4zjbf4zp5d7puada3aoy