  Previous approaches for scene text detection usually rely on manually defined
sliding windows. This work presents an intuitive two-stage region-based method
to detect multi-oriented text without any prior knowledge regarding the textual
shape. In the first stage, we estimate the possible locations of text instances
by detecting and linking corners instead of shifting a set of default anchors.
The quadrilateral proposals are geometry adaptive, which allows our method to
cope with various text aspect ratios and orientations. In the second stage, we
design a new pooling layer named Dual-RoI Pooling which embeds data
augmentation inside the region-wise subnetwork for more robust classification
and regression over these proposals. Experimental results on public benchmarks
confirm that the proposed method is capable of achieving comparable performance
with state-of-the-art methods. The code is publicly available at
https://github.com/xhzdeng/crpn
