Feature Enhancement Network: A Refined Scene Text Detector

In this paper, we propose a refined scene text detector with a novel Feature Enhancement Network (FEN) for Region Proposal and Text Detection Refinement. Retrospectively, both region proposal with only 3 × 3 sliding-window feature and text detection refinement with single scale high level feature are insufficient, especially for smaller scene text. Therefore, we design a new FEN network with task-specific, low and high level semantic features fusion to improve the performance of text detection. Besides, since unitary position-sensitive RoI pooling in general object detection is unreasonable for variable text regions, an adaptively weighted position-sensitive RoI pooling layer is devised for further enhancing the detecting accuracy. To tackle the sample-imbalance problem during the refinement stage, we also propose an effective 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-theart results, outperforming all reported methods in terms of F-measure.