Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: An Anchor-Free Region Proposal Network for Faster R-CNN based Text
  Detection Approaches   The anchor mechanism of Faster R-CNN and SSD framework is considered not
effective enough to scene text detection, which can be attributed to its IoU
based matching criterion between anchors and ground-truth boxes. In order to
better enclose scene text instances of various shapes, it requires to design
anchors of various scales, aspect ratios and even orientations manually, which
makes anchor-based methods sophisticated and inefficient. In this paper, we
propose a novel anchor-free region proposal network (AF-RPN) to replace the
original anchor-based RPN in the Faster R-CNN framework to address the above
problem. Compared with a vanilla RPN and FPN-RPN, AF-RPN can get rid of
complicated anchor design and achieve higher recall rate on large-scale
COCO-Text dataset. Owing to the high-quality text proposals, our Faster R-CNN
based two-stage text detection approach achieves state-of-the-art results on
ICDAR-2017 MLT, ICDAR-2015 and ICDAR-2013 text detection benchmarks when using
single-scale and single-model (ResNet50) testing only.

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The categories of this paper are: math
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The categories of this paper are: hep-ex
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The categories of this paper are: math-ph
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The categories of this paper are: gr-qc
33
The categories of this paper are: plasm-ph
18
The categories of this paper are: econ
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The categories of this paper are: cmp-lg
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The categories of this paper are: mtrl-th
30
The categories of this paper are: q-alg
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The categories of this paper are: q-fin
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The categories of this paper are: hep-lat
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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: astro-ph
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The categories of this paper are: bayes-an
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The categories of this paper are: cond-mat