Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: All You Need Is Boundary: Toward Arbitrary-Shaped Text Spotting   Recently, end-to-end text spotting that aims to detect and recognize text
from cluttered images simultaneously has received particularly growing interest
in computer vision. Different from the existing approaches that formulate text
detection as bounding box extraction or instance segmentation, we localize a
set of points on the boundary of each text instance. With the representation of
such boundary points, we establish a simple yet effective scheme for end-to-end
text spotting, which can read the text of arbitrary shapes. Experiments on
three challenging datasets, including ICDAR2015, TotalText and COCO-Text
demonstrate that the proposed method consistently surpasses the
state-of-the-art in both scene text detection and end-to-end text recognition
tasks.

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The categories of this paper are: comp-gas
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The categories of this paper are: plasm-ph
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The categories of this paper are: hep-th
3
The categories of this paper are: physics
0
The categories of this paper are: hep-ph
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The categories of this paper are: hep-lat
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The categories of this paper are: supr-con
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The categories of this paper are: q-fin
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The categories of this paper are: ao-sci
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The categories of this paper are: adap-org
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The categories of this paper are: funct-an
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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: q-alg
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The categories of this paper are: solv-int
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The categories of this paper are: nucl-ex