Feature Enhancement Network: A Refined Scene Text Detector


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




Feature Enhancement Network, Text Detection, Region Proposal, adaptively weighted position-sensitive RoI pooling, positives mining


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 x 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 ICDAR2011 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.




How to Cite

Zhang, S., Liu, Y., Jin, L., & Luo, C. (2018). Feature Enhancement Network: A Refined Scene Text Detector. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). https://doi.org/10.1609/aaai.v32i1.11887



Main Track: Machine Learning Applications