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.

该文提出一种基于神经网络的边界生成方法，能够直接生成准确的任意形状文本边界而无需后处理，实验表明该方法在公开数据集上取得了良好的效果。

自适应边界建议网络用于任意形状文本检测

Adaptive Boundary Proposal Network for Arbitrary Shape Text Detection

Scene text recognition with arbitrary shape is very challenging due to large
variations in text shapes, fonts, colors, backgrounds, etc. Most
state-of-the-art algorithms rectify the input image into the normalized image,
then treat the recognition as a sequence prediction task. The bottleneck of
such methods is the rectification, which will cause errors due to distortion
perspective. In this paper, we find that the rectification is completely
unnecessary. What all we need is the spatial attention. We therefore propose a
simple but extremely effective scene text recognition method based on
transformer [50]. Different from previous transformer based models [56,34],
which just use the decoder of the transformer to decode the convolutional
attention, the proposed method use a convolutional feature maps as word
embedding input into transformer. In such a way, our method is able to make
full use of the powerful attention mechanism of the transformer. Extensive
experimental results show that the proposed method significantly outperforms
state-of-the-art methods by a very large margin on both regular and irregular
text datasets. On one of the most challenging CUTE dataset whose
state-of-the-art prediction accuracy is 89.6%, our method achieves 99.3%, which
is a pretty surprising result. We will release our source code and believe that
our method will be a new benchmark of scene text recognition with arbitrary
shapes.

本论文提出了一种基于 transformer 的简单但极其有效的场景文本识别方法，只需要空间注意力而不需要矫正图像，仅使用卷积特征图作为单词嵌入输入到 transformer 中，并在大规模实验中取得了显著的优越性能。