IOS-Net: An inside-to-outside supervision network for scale robust text detection in the wild

作者：

Highlights：

• We propose an inside-to-outside supervision network to detect text in natural scenes.

• Our method can comprehensively tackle the R-scale and S-scale problems.

• Our method achieves a state-of-the-art performance on three public benchmarks.

• Our method is the best text detector with respect to the speed and F-score trade-off.

摘要

•We propose an inside-to-outside supervision network to detect text in natural scenes.•Our method can comprehensively tackle the R-scale and S-scale problems.•Our method achieves a state-of-the-art performance on three public benchmarks.•Our method is the best text detector with respect to the speed and F-score trade-off.

论文关键词：Text detection,Various sizes,Diverse aspect ratios,Inside-to-outside supervision,Position-sensitive segmentation

论文评审过程：Received 28 January 2019, Revised 2 January 2020, Accepted 23 February 2020, Available online 4 March 2020, Version of Record 11 March 2020.

论文官网地址：https://doi.org/10.1016/j.patcog.2020.107304