In this work, we present DeepEraser, an effective deep network for generic
text removal. DeepEraser utilizes a recurrent architecture that erases the text
in an image via iterative operations. Our idea comes from the process of
erasing pencil script, where the text area designated for removal is subject to
continuous monitoring and the text is attenuated progressively, ensuring a
thorough and clean erasure. Technically, at each iteration, an innovative
erasing module is deployed, which not only explicitly aggregates the previous
erasing progress but also mines additional semantic context to erase the target
text. Through iterative refinements, the text regions are progressively
replaced with more appropriate content and finally converge to a relatively
accurate status. Furthermore, a custom mask generation strategy is introduced
to improve the capability of DeepEraser for adaptive text removal, as opposed
to indiscriminately removing all the text in an image. Our DeepEraser is
notably compact with only 1.4M parameters and trained in an end-to-end manner.
To verify its effectiveness, extensive experiments are conducted on several
prevalent benchmarks, including SCUT-Syn, SCUT-EnsText, and Oxford Synthetic
text dataset. The quantitative and qualitative results demonstrate the
effectiveness of our DeepEraser over the state-of-the-art methods, as well as
its strong generalization ability in custom mask text removal. The codes and
pre-trained models are available at this https URL

通过迭代操作来擦除图像中的文本，并引入了创新的擦除模块和自定义掩码生成策略，实现了具有强大通用性的 DeepEraser。

深层擦除：用于通用文本消除的深度迭代上下文挖掘

DeepEraser: Deep Iterative Context Mining for Generic Text Eraser

Text removal algorithms have been proposed for uni-lingual scripts with
regular shapes and layouts. However, to the best of our knowledge, a generic
text removal method which is able to remove all or user-specified text regions
regardless of font, script, language or shape is not available. Developing such
a generic text eraser for real scenes is a challenging task, since it inherits
all the challenges of multi-lingual and curved text detection and inpainting.
To fill this gap, we propose a mask-based text removal network (MTRNet). MTRNet
is a conditional adversarial generative network (cGAN) with an auxiliary mask.
The introduced auxiliary mask not only makes the cGAN a generic text eraser,
but also enables stable training and early convergence on a challenging
large-scale synthetic dataset, initially proposed for text detection in real
scenes. What's more, MTRNet achieves state-of-the-art results on several
real-world datasets including ICDAR 2013, ICDAR 2017 MLT, and CTW1500, without
being explicitly trained on this data, outperforming previous state-of-the-art
methods trained directly on these datasets.

提出了一种基于掩模的文字删除方法（MTRNet），作为一种有条件的对抗性生成网络（cGAN），配备辅助掩模使其成为通用文本擦除工具，该算法在多个真实数据集上实现了最先进的结果。

MTRNet: 通用场景文本擦除器

MTRNet: A Generic Scene Text Eraser

A new method is proposed for removing text from natural images. The challenge
is to first accurately localize text on the stroke-level and then replace it
with a visually plausible background. Unlike previous methods that require
image patches to erase scene text, our method, namely ensconce network
(EnsNet), can operate end-to-end on a single image without any prior knowledge.
The overall structure is an end-to-end trainable FCN-ResNet-18 network with a
conditional generative adversarial network (cGAN). The feature of the former is
first enhanced by a novel lateral connection structure and then refined by four
carefully designed losses: multiscale regression loss and content loss, which
capture the global discrepancy of different level features; texture loss and
total variation loss, which primarily target filling the text region and
preserving the reality of the background. The latter is a novel local-sensitive
GAN, which attentively assesses the local consistency of the text erased
regions. Both qualitative and quantitative sensitivity experiments on synthetic
images and the ICDAR 2013 dataset demonstrate that each component of the EnsNet
is essential to achieve a good performance. Moreover, our EnsNet can
significantly outperform previous state-of-the-art methods in terms of all
metrics. In addition, a qualitative experiment conducted on the SMBNet dataset
further demonstrates that the proposed method can also preform well on general
object (such as pedestrians) removal tasks. EnsNet is extremely fast, which can
preform at 333 fps on an i5-8600 CPU device.

本文提出了一种新的方法来移除自然图像上的文本，该方法使用 ensconce 网络（EnsNet），可以在单个图像上端到端地运行，无需任何先前知识，并使用多尺度回归损失，纹理损失和总变差损失等四个损失函数来增强功能。与以前的方法不同的是，EnsNet 可以在插入场景文本的图像补丁之前准确地分别在笔画级别上定位文本，然后将其替换为具有视觉上合理的背景。从合成图像和 ICDAR 2013 数据集的定性和定量灵敏度实验以及针对 SMBNet 数据集的定性实验结果表明，EnsNet 的每个组件对于实现良好性能都是必不可少的，并且在所有指标方面均明显优于先前最先进的方法。此外，该方法还可以在一般物体（如行人）的去除任务上表现良好。