  Scene text detection has received attention for years and achieved an
impressive performance across various benchmarks. In this work, we propose an
efficient and accurate approach to detect multioriented text in scene images.
The proposed feature fusion mechanism allows us to use a shallower network to
reduce the computational complexity. A self-attention mechanism is adopted to
suppress false positive detections. Experiments on public benchmarks including
ICDAR 2013, ICDAR 2015 and MSRA-TD500 show that our proposed approach can
achieve better or comparable performances with fewer parameters and less
computational cost.
