Many scene text recognition approaches are based on purely visual information
and ignore the semantic relation between scene and text. In this paper, we
tackle this problem from natural language processing perspective to fill the
gap between language and vision. We propose a post-processing approach to
improve scene text recognition accuracy by using occurrence probabilities of
words (unigram language model), and the semantic correlation between scene and
text. For this, we initially rely on an off-the-shelf deep neural network,
already trained with a large amount of data, which provides a series of text
hypotheses per input image. These hypotheses are then re-ranked using word
frequencies and semantic relatedness with objects or scenes in the image. As a
result of this combination, the performance of the original network is boosted
with almost no additional cost. We validate our approach on ICDAR'17 dataset.Comment: Accepted by ACCV 2018. arXiv admin note: substantial text overlap
with arXiv:1810.0977