NDLI: Using a Probabilistic Syllable Model to Improve Scene Text Recognition

Content Provider	IEEE Xplore Digital Library
Author	Feild, J.L. Learned-Miller, E.G. Smith, D.A.
Copyright Year	2013
Description	Author affiliation: Univ. of Massachusetts, Amherst, MA, USA (Feild, J.L.; Learned-Miller, E.G.)
Abstract	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.
Starting Page	897
Ending Page	901
File Size	342620
Page Count	5
File Format	PDF
ISBN	9780769549996
ISSN	15205363
DOI	10.1109/ICDAR.2013.183
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-08-25
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Hidden Markov models Probabilistic logic Grammar Dictionaries Text recognition Data models Computational modeling
Content Type	Text
Resource Type	Article

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