  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 Total-Text.
