In this paper, we present a new Mask R-CNN based text detection approach
which can robustly detect multi-oriented and curved text from natural scene
images in a unified manner. To enhance the feature representation ability of
Mask R-CNN for text detection tasks, we propose to use the Pyramid Attention
Network (PAN) as a new backbone network of Mask R-CNN. Experiments demonstrate
that PAN can suppress false alarms caused by text-like backgrounds more
effectively. Our proposed approach has achieved superior performance on both
multi-oriented (ICDAR-2015, ICDAR-2017 MLT) and curved (SCUT-CTW1500) text
detection benchmark tasks by only using single-scale and single-model testing.

本文提出了基于 Mask R-CNN 技术的文本检测新方法，应用金字塔注意力网络作为新的 Mask R-CNN 骨干网络以提高其特征表达能力，该方法可以统一地在自然场景图像中稳健地检测多定向和曲线文本，并在多种测试数据集上获得优越的性能。

使用金字塔注意网络的 Mask R-CNN 进行场景文字检测

Mask R-CNN with Pyramid Attention Network for Scene Text Detection

Incidental scene text detection, especially for multi-oriented text regions,
is one of the most challenging tasks in many computer vision applications.
Different from the common object detection task, scene text often suffers from
a large variance of aspect ratio, scale, and orientation. To solve this
problem, we propose a novel end-to-end scene text detector IncepText from an
instance-aware segmentation perspective. We design a novel Inception-Text
module and introduce deformable PSROI pooling to deal with multi-oriented text
detection. Extensive experiments on ICDAR2015, RCTW-17, and MSRA-TD500 datasets
demonstrate our method's superiority in terms of both effectiveness and
efficiency. Our proposed method achieves 1st place result on ICDAR2015
challenge and the state-of-the-art performance on other datasets. Moreover, we
have released our implementation as an OCR product which is available for
public access.

从实例感知的角度出发，我们提出了一种新的端到端场景文本检测器 IncepText，利用 Inception-Text 模块和可变形 PSROI 池化处理多方向文本区域，实现在 ICDAR2015，RCTW-17 和 MSRA-TD500 数据集上取得了最先进性能的结果，并将其作为 OCR 产品开源。

IncepText: 一种新的具有可变形 PSROI 池化的 Inception-Text 模块，用于多方位场景文本检测

IncepText: A New Inception-Text Module with Deformable PSROI Pooling for  Multi-Oriented Scene Text Detection

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。