FC<sup>2</sup>RN: A Fully Convolutional Corner Refinement Network for Accurate Multi-Oriented Scene Text Detection
Xugong Qin, Yu Zhou, Youhui Guo, Dayan Wu, Weiping Wang
Abstract
Accurate detection of multi-oriented text that accounts for a large proportion in real practice is of great significance. The performance has improved rapidly on common benchmarks in recent years. However, dense long text case and the quality of detection are easy to be overlooked. Direct regression may produce low-quality and incomplete detections due to the constrain of the receptive field; proposal-based methods could alleviate this but might introduce redundant context due to RoI operation, degrading the performance. To address the dilemma, a novel proposed corner-aware convolution in which the sampling positions tightly cover the text area is utilized to encode an initial corner prediction into the feature maps, which can be further used to produce a refined corner prediction. We embed the proposed module into an anchor-free baseline model, leading to a simple and effective fully convolutional corner refinement network (FC <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> RN). Experimental results on four public datasets including MSRATD500, ICDAR2015, RCTW-17, and COCO-Text demonstrate that FC <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> RN can outperform state-of-the-art methods.