Scene Text Detection with Supervised Pyramid Context Network


  • Enze Xie Tongji University
  • Yuhang Zang University of Electronic Science and Technology of China
  • Shuai Shao Face++
  • Gang Yu Face++
  • Cong Yao Megvii
  • Guangyao Li Tongji University



Scene text detection methods based on deep learning have achieved remarkable results over the past years. However, due to the high diversity and complexity of natural scenes, previous state-of-the-art text detection methods may still produce a considerable amount of false positives, when applied to images captured in real-world environments. To tackle this issue, mainly inspired by Mask R-CNN, we propose in this paper an effective model for scene text detection, which is based on Feature Pyramid Network (FPN) and instance segmentation. We propose a supervised pyramid context network (SPCNET) to precisely locate text regions while suppressing false positives.

Benefited from the guidance of semantic information and sharing FPN, SPCNET obtains significantly enhanced performance while introducing marginal extra computation. Experiments on standard datasets demonstrate that our SPCNET clearly outperforms start-of-the-art methods. Specifically, it achieves an F-measure of 92.1% on ICDAR2013, 87.2% on ICDAR2015, 74.1% on ICDAR2017 MLT and 82.9% on




How to Cite

Xie, E., Zang, Y., Shao, S., Yu, G., Yao, C., & Li, G. (2019). Scene Text Detection with Supervised Pyramid Context Network. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 9038-9045.



AAAI Technical Track: Vision