Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Text Recognition -- Real World Data and Where to Find Them   We present a method for exploiting weakly annotated images to improve text
extraction pipelines. The approach uses an arbitrary end-to-end text
recognition system to obtain text region proposals and their, possibly
erroneous, transcriptions. The proposed method includes matching of imprecise
transcription to weak annotations and edit distance guided neighbourhood
search. It produces nearly error-free, localised instances of scene text, which
we treat as "pseudo ground truth" (PGT).
  We apply the method to two weakly-annotated datasets. Training with the
extracted PGT consistently improves the accuracy of a state of the art
recognition model, by 3.7~\% on average, across different benchmark datasets
(image domains) and 24.5~\% on one of the weakly annotated datasets.

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The categories of this paper are: ao-sci
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The categories of this paper are: astro-ph
4
The categories of this paper are: cond-mat
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The categories of this paper are: econ
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The categories of this paper are: supr-con
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The categories of this paper are: mtrl-th
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The categories of this paper are: nucl-th
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The categories of this paper are: cmp-lg
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The categories of this paper are: chao-dyn
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The categories of this paper are: stat
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The categories of this paper are: funct-an
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The categories of this paper are: eess
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The categories of this paper are: hep-lat
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The categories of this paper are: acc-phys
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The categories of this paper are: physics
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The categories of this paper are: math-ph