  Recent scene text detection works mainly focus on curve text detection.
However, in real applications, the curve texts are more scarce than the
multi-oriented ones. Accurate detection of multi-oriented text with large
variations of scales, orientations, and aspect ratios is of great significance.
Among the multi-oriented detection methods, direct regression for the geometry
of scene text shares a simple yet powerful pipeline and gets popular in
academic and industrial communities, but it may produce imperfect detections,
especially for long texts due to the limitation of the receptive field. In this
work, we aim to improve this while keeping the pipeline simple. A fully
convolutional corner refinement network (FC2RN) is proposed for accurate
multi-oriented text detection, in which an initial corner prediction and a
refined corner prediction are obtained at one pass. With a novel quadrilateral
RoI convolution operation tailed for multi-oriented scene text, the initial
quadrilateral prediction is encoded into the feature maps which can be further
used to predict offset between the initial prediction and the ground-truth as
well as output a refined confidence score. Experimental results on four public
datasets including MSRA-TD500, ICDAR2017-RCTW, ICDAR2015, and COCO-Text
demonstrate that FC2RN can outperform the state-of-the-art methods. The
ablation study shows the effectiveness of corner refinement and scoring for
accurate text localization.
