  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.
