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 taskspecific, 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 ef- ficiently 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.

Authors

• Sheng Zhang
• Yuliang Liu Huazhong University of Science and Technology
• Lianwen Jin South China University of Technology
• Canjie Luo

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