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Feature Enhancement Network: A Refined Scene Text Detector
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} 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.Comment: 8 pages, 5 figures, 2 tables. This paper is accepted to appear in
AAAI 201
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