Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Real-time Scene Text Detection Based on Global Level and Word Level
  Features   It is an extremely challenging task to detect arbitrary shape text in natural
scenes on high accuracy and efficiency. In this paper, we propose a scene text
detection framework, namely GWNet, which mainly includes two modules: Global
module and RCNN module. Specifically, Global module improves the adaptive
performance of the DB (Differentiable Binarization) module by adding k
submodule and shift submodule. Two submodules enhance the adaptability of
amplifying factor k, accelerate the convergence of models and help to produce
more accurate detection results. RCNN module fuses global-level and word-level
features. The word-level label is generated by obtaining the minimum
axis-aligned rectangle boxes of the shrunk polygon. In the inference period,
GWNet only uses global-level features to output simple polygon detections.
Experiments on four benchmark datasets, including the MSRA-TD500, Total-Text,
ICDAR2015 and CTW-1500, demonstrate that our GWNet outperforms the
state-of-the-art detectors. Specifically, with a backbone of ResNet-50, we
achieve an F-measure of 88.6% on MSRA- TD500, 87.9% on Total-Text, 89.2% on
ICDAR2015 and 87.5% on CTW-1500.

18
The categories of this paper are: econ
34
The categories of this paper are: chem-ph
23
The categories of this paper are: solv-int
9
The categories of this paper are: nlin
22
The categories of this paper are: chao-dyn
13
The categories of this paper are: quant-ph
12
The categories of this paper are: nucl-th
33
The categories of this paper are: plasm-ph
3
The categories of this paper are: physics
5
The categories of this paper are: gr-qc
27
The categories of this paper are: comp-gas
14
The categories of this paper are: math-ph
26
The categories of this paper are: bayes-an
30
The categories of this paper are: q-alg
37
The categories of this paper are: supr-con
16
The categories of this paper are: stat