  Scene text detection has drawn the close attention of researchers. Though
many methods have been proposed for horizontal and oriented texts, previous
methods may not perform well when dealing with arbitrary-shaped texts such as
curved texts. In particular, confusion problem arises in the case of nearby
text instances. In this paper, we propose a simple yet effective method for
accurate arbitrary-shaped nearby scene text detection. Firstly, a One-to-Many
Training Scheme (OMTS) is designed to eliminate confusion and enable the
proposals to learn more appropriate groundtruths in the case of nearby text
instances. Secondly, we propose a Proposal Feature Attention Module (PFAM) to
exploit more effective features for each proposal, which can better adapt to
arbitrary-shaped text instances. Finally, we propose a baseline that is based
on Faster R-CNN and outputs the curve representation directly. Equipped with
PFAM and OMTS, the detector can achieve state-of-the-art or competitive
performance on several challenging benchmarks.
