The reading of arbitrarily-shaped text has received increasing research
attention. However, existing text spotters are mostly built on two-stage
frameworks or character-based methods, which suffer from either Non-Maximum
Suppression (NMS), Region-of-Interest (RoI) operations, or character-level
annotations. In this paper, to address the above problems, we propose a novel
fully convolutional Point Gathering Network (PGNet) for reading
arbitrarily-shaped text in real-time. The PGNet is a single-shot text spotter,
where the pixel-level character classification map is learned with proposed
PG-CTC loss avoiding the usage of character-level annotations. With a PG-CTC
decoder, we gather high-level character classification vectors from
two-dimensional space and decode them into text symbols without NMS and RoI
operations involved, which guarantees high efficiency. Additionally, reasoning
the relations between each character and its neighbors, a graph refinement
module (GRM) is proposed to optimize the coarse recognition and improve the
end-to-end performance. Experiments prove that the proposed method achieves
competitive accuracy, meanwhile significantly improving the running speed. In
particular, in Total-Text, it runs at 46.7 FPS, surpassing the previous
spotters with a large margin.

本文提出了全卷积的点聚集网络（PGNet）用于实时读取任意形状的文字，并且在减少 NMS 和 RoI 操作的同时，通过提出的 PG-CTC 损失对像素级别的字符分类图进行学习，避免使用字符级别注释。同时，通过建立字符与其相邻字符之间的联系，引入图形优化模块（GRM）以优化粗糙的识别并提高端到端性能。实验结果证明了所提出的方法具有竞争性的准确性，同时显著提高了运行速度。