Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Visual Re-ranking with Natural Language Understanding for Text Spotting   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.

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The categories of this paper are: ao-sci
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The categories of this paper are: hep-ph
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The categories of this paper are: dg-ga
35
The categories of this paper are: mtrl-th
3
The categories of this paper are: physics
37
The categories of this paper are: supr-con
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The categories of this paper are: patt-sol
32
The categories of this paper are: atom-ph
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The categories of this paper are: hep-lat
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The categories of this paper are: eess
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The categories of this paper are: nucl-th
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The categories of this paper are: acc-phys
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The categories of this paper are: nucl-ex
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The categories of this paper are: gr-qc
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The categories of this paper are: comp-gas
2
The categories of this paper are: math