  Object proposal technique with dense anchoring scheme for scene text
detection were applied frequently to achieve high recall. It results in the
significant improvement in accuracy but waste of computational searching,
regression and classification. In this paper, we propose an anchor
selection-based region proposal network (AS-RPN) using effective selected
anchors instead of dense anchors to extract text proposals. The center, scales,
aspect ratios and orientations of anchors are learnable instead of fixing,
which leads to high recall and greatly reduced numbers of anchors. By replacing
the anchor-based RPN in Faster RCNN, the AS-RPN-based Faster RCNN can achieve
comparable performance with previous state-of-the-art text detecting approaches
on standard benchmarks, including COCO-Text, ICDAR2013, ICDAR2015 and
MSRA-TD500 when using single-scale and single model (ResNet50) testing only.
