  Arbitrary shape text detection is a challenging task due to the high
complexity and variety of scene texts. In this work, we propose a novel
adaptive boundary proposal network for arbitrary shape text detection, which
can learn to directly produce accurate boundary for arbitrary shape text
without any post-processing. Our method mainly consists of a boundary proposal
model and an innovative adaptive boundary deformation model. The boundary
proposal model constructed by multi-layer dilated convolutions is adopted to
produce prior information (including classification map, distance field, and
direction field) and coarse boundary proposals. The adaptive boundary
deformation model is an encoder-decoder network, in which the encoder mainly
consists of a Graph Convolutional Network (GCN) and a Recurrent Neural Network
(RNN). It aims to perform boundary deformation in an iterative way for
obtaining text instance shape guided by prior information from the boundary
proposal model. In this way, our method can directly and efficiently generate
accurate text boundaries without complex post-processing. Extensive experiments
on publicly available datasets demonstrate the state-of-the-art performance of
our method.
