Recently, scene text detection has received significant attention due to its
wide application. However, accurate detection in complex scenes of multiple
scales, orientations, and curvature remains a challenge. Numerous detection
methods adopt the Vatti clipping (VC) algorithm for multiple-instance training
to address the issue of arbitrary-shaped text. Yet we identify several bias
results from these approaches called the "shrinked kernel". Specifically, it
refers to a decrease in accuracy resulting from an output that overly favors
the text kernel. In this paper, we propose a new approach named Expand Kernel
Network (EK-Net) with expand kernel distance to compensate for the previous
deficiency, which includes three-stages regression to complete instance
detection. Moreover, EK-Net not only realize the precise positioning of
arbitrary-shaped text, but also achieve a trade-off between performance and
speed. Evaluation results demonstrate that EK-Net achieves state-of-the-art or
competitive performance compared to other advanced methods, e.g., F-measure of
85.72% at 35.42 FPS on ICDAR 2015, F-measure of 85.75% at 40.13 FPS on CTW1500.

在本文中，我们提出了一种名为 Expand Kernel Network (EK-Net) 的新方法，该方法通过采用扩展内核距离解决了现有方法中存在的偏差问题，包括三阶段回归完成实例检测。此外，EK-Net 不仅可以实现任意形状文本的精确定位，还能在性能和速度之间取得平衡。评估结果表明，与其他先进方法相比，EK-Net 在 ICDAR 2015 上以 35.42 FPS 的 F-measure 为 85.72%，在 CTW1500 上以 40.13 FPS 的 F-measure 为 85.75%。

EK-Net：基于扩展核距离的实时场景文本检测

EK-Net:Real-time Scene Text Detection with Expand Kernel Distance

One of the main challenges for arbitrary-shaped text detection is to design a
good text instance representation that allows networks to learn diverse text
geometry variances. Most of existing methods model text instances in image
spatial domain via masks or contour point sequences in the Cartesian or the
polar coordinate system. However, the mask representation might lead to
expensive post-processing, while the point sequence one may have limited
capability to model texts with highly-curved shapes. To tackle these problems,
we model text instances in the Fourier domain and propose one novel Fourier
Contour Embedding (FCE) method to represent arbitrary shaped text contours as
compact signatures. We further construct FCENet with a backbone, feature
pyramid networks (FPN) and a simple post-processing with the Inverse Fourier
Transformation (IFT) and Non-Maximum Suppression (NMS). Different from previous
methods, FCENet first predicts compact Fourier signatures of text instances,
and then reconstructs text contours via IFT and NMS during test. Extensive
experiments demonstrate that FCE is accurate and robust to fit contours of
scene texts even with highly-curved shapes, and also validate the effectiveness
and the good generalization of FCENet for arbitrary-shaped text detection.
Furthermore, experimental results show that our FCENet is superior to the
state-of-the-art (SOTA) methods on CTW1500 and Total-Text, especially on
challenging highly-curved text subset.

该研究论文提出了一种称为 Fourier Contour Embedding (FCE) 方法，通过傅里叶变换将任意形状的文本轮廓表示为紧凑的签名，并构建 FCENet 模型进行文本检测，实验结果表明其准确度和鲁棒性均较高。

傅立叶轮廓嵌入在任意形状文本检测中的应用

Fourier Contour Embedding for Arbitrary-Shaped Text Detection

This paper reports the ICDAR2019 Robust Reading Challenge on Arbitrary-Shaped
Text (RRC-ArT) that consists of three major challenges: i) scene text
detection, ii) scene text recognition, and iii) scene text spotting. A total of
78 submissions from 46 unique teams/individuals were received for this
competition. The top performing score of each challenge is as follows: i) T1 -
82.65%, ii) T2.1 - 74.3%, iii) T2.2 - 85.32%, iv) T3.1 - 53.86%, and v) T3.2 -
54.91%. Apart from the results, this paper also details the ArT dataset, tasks
description, evaluation metrics and participants methods. The dataset, the
evaluation kit as well as the results are publicly available at
this https URL

本文报道了 ICDAR2019 关于任意形状文本 (RRC-ArT) 的鲁棒性阅读挑战，重点包括场景文本检测、场景文本识别、场景文本识别，并介绍了该挑战的数据集、任务描述、评估指标和参与者方法。