CPN: Complementary Proposal Network for Unconstrained Text Detection
Longhuang Wu Shangxuan Tian Youxin Wang Pengfei Xiong
Existing methods for scene text detection can be divided into two paradigms:
segmentation-based and anchor-based. While Segmentation-based methods are
well-suited for irregular shapes, they struggle with compact or overlapping
layouts. Conversely, anchor-based approaches excel for complex layouts but
suffer from irregular shapes. To strengthen their merits and overcome their
respective demerits, we propose a Complementary Proposal Network (CPN) that
seamlessly and parallelly integrates semantic and geometric information for
superior performance. The CPN comprises two efficient networks for proposal
generation: the Deformable Morphology Semantic Network, which generates
semantic proposals employing an innovative deformable morphological operator,
and the Balanced Region Proposal Network, which produces geometric proposals
with pre-defined anchors. To further enhance the complementarity, we introduce
an Interleaved Feature Attention module that enables semantic and geometric
features to interact deeply before proposal generation. By leveraging both
complementary proposals and features, CPN outperforms state-of-the-art
approaches with significant margins under comparable computation cost.
Specifically, our approach achieves improvements of 3.6%, 1.3% and 1.0% on
challenging benchmarks ICDAR19-ArT, IC15, and MSRA-TD500, respectively. Code
for our method will be released.