Recently, scene text detection has become an active research topic in
computer vision and document analysis, because of its great importance and
significant challenge. However, vast majority of the existing methods detect
text within local regions, typically through extracting character, word or line
level candidates followed by candidate aggregation and false positive
elimination, which potentially exclude the effect of wide-scope and long-range
contextual cues in the scene. To take full advantage of the rich information
available in the whole natural image, we propose to localize text in a holistic
manner, by casting scene text detection as a semantic segmentation problem. The
proposed algorithm directly runs on full images and produces global, pixel-wise
prediction maps, in which detections are subsequently formed. To better make
use of the properties of text, three types of information regarding text
region, individual characters and their relationship are estimated, with a
single Fully Convolutional Network (FCN) model. With such predictions of text
properties, the proposed algorithm can simultaneously handle horizontal,
multi-oriented and curved text in real-world natural images. The experiments on
standard benchmarks, including ICDAR 2013, ICDAR 2015 and MSRA-TD500,
demonstrate that the proposed algorithm substantially outperforms previous
state-of-the-art approaches. Moreover, we report the first baseline result on
the recently-released, large-scale dataset COCO-Text.

该文提出了一种基于全局的语义分割方法来定位场景中的文本，使用单一的 FCN 模型估计文本属性，以同时处理水平，多方向和曲线文本，并在 ICDAR 2013、ICDAR 2015、MSRA-TD500 以及 COCO-Text 数据集上测试得到优于现有最先进方法的结果。