Improving Open-Vocabulary Scene Text Recognition

This paper presents a system for open-vocabulary text recognition in images of natural scenes. First, we describe a novel technique for text segmentation that models smooth color changes across images. We combine this with a recognition component based on a conditional random field with histogram of oriented gradients descriptors and incorporate language information from a lexicon to improve recognition performance. Many existing techniques for this problem use language information from a standard lexicon, but these may not include many of the words found in images of the environment, such as storefront signs and street signs. We avoid this limitation by incorporating language information from a large web-based lexicon of around 13.5 million words. This lexicon contains words encountered during a crawl of the web, so it is likely to contain proper nouns, like business names and street names. We show that our text segmentation method allows for better recognition performance than the current state-of-the-art text segmentation method. We also evaluate this full system on two standard data sets, ICDAR 2003 and ICDAR 2011, and show an increase in word recognition performance compared to the current state-of-the-art methods.