  In this paper, we present a new Mask R-CNN based text detection approach
which can robustly detect multi-oriented and curved text from natural scene
images in a unified manner. To enhance the feature representation ability of
Mask R-CNN for text detection tasks, we propose to use the Pyramid Attention
Network (PAN) as a new backbone network of Mask R-CNN. Experiments demonstrate
that PAN can suppress false alarms caused by text-like backgrounds more
effectively. Our proposed approach has achieved superior performance on both
multi-oriented (ICDAR-2015, ICDAR-2017 MLT) and curved (SCUT-CTW1500) text
detection benchmark tasks by only using single-scale and single-model testing.
