Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Cascaded Segmentation-Detection Networks for Word-Level Text Spotting   We introduce an algorithm for word-level text spotting that is able to
accurately and reliably determine the bounding regions of individual words of
text "in the wild". Our system is formed by the cascade of two convolutional
neural networks. The first network is fully convolutional and is in charge of
detecting areas containing text. This results in a very reliable but possibly
inaccurate segmentation of the input image. The second network (inspired by the
popular YOLO architecture) analyzes each segment produced in the first stage,
and predicts oriented rectangular regions containing individual words. No
post-processing (e.g. text line grouping) is necessary. With execution time of
450 ms for a 1000-by-560 image on a Titan X GPU, our system achieves the
highest score to date among published algorithms on the ICDAR 2015 Incidental
Scene Text dataset benchmark.

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The categories of this paper are: eess
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The categories of this paper are: adap-org
0
The categories of this paper are: hep-ph
12
The categories of this paper are: nucl-th
3
The categories of this paper are: physics
36
The categories of this paper are: cmp-lg
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The categories of this paper are: hep-lat
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The categories of this paper are: nlin
2
The categories of this paper are: math
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The categories of this paper are: stat
30
The categories of this paper are: q-alg
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The categories of this paper are: supr-con
20
The categories of this paper are: acc-phys
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The categories of this paper are: dg-ga
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The categories of this paper are: ao-sci
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The categories of this paper are: comp-gas