  In this paper, we propose a refined scene text detector with a \textit{novel}
Feature Enhancement Network (FEN) for Region Proposal and Text Detection
Refinement. Retrospectively, both region proposal with \textit{only} $3\times
3$ sliding-window feature and text detection refinement with \textit{single
scale} high level feature are insufficient, especially for smaller scene text.
Therefore, we design a new FEN network with \textit{task-specific},
\textit{low} and \textit{high} level semantic features fusion to improve the
performance of text detection. Besides, since \textit{unitary}
position-sensitive RoI pooling in general object detection is unreasonable for
variable text regions, an \textit{adaptively weighted} position-sensitive RoI
pooling layer is devised for further enhancing the detecting accuracy. To
tackle the \textit{sample-imbalance} problem during the refinement stage, we
also propose an effective \textit{positives mining} strategy for efficiently
training our network. Experiments on ICDAR 2011 and 2013 robust text detection
benchmarks demonstrate that our method can achieve state-of-the-art results,
outperforming all reported methods in terms of F-measure.
