Recent efforts on scene text erasing have shown promising results. However,
existing methods require rich yet costly label annotations to obtain robust
models, which limits the use for practical applications. To this end, we study
an unsupervised scenario by proposing a novel Self-supervised Text Erasing
(STE) framework that jointly learns to synthesize training images with erasure
ground-truth and accurately erase texts in the real world. We first design a
style-aware image synthesis function to generate synthetic images with diverse
styled texts based on two synthetic mechanisms. To bridge the text style gap
between the synthetic and real-world data, a policy network is constructed to
control the synthetic mechanisms by picking style parameters with the guidance
of two specifically designed rewards. The synthetic training images with
erasure ground-truth are then fed to train a coarse-to-fine erasing network. To
produce better erasing outputs, a triplet erasure loss is designed to enforce
the refinement stage to recover background textures. Moreover, we provide a new
dataset (called PosterErase), which contains 60K high-resolution posters with
texts and is more challenging for the text erasing task. The proposed method
has been extensively evaluated with both PosterErase and the widely-used
SCUT-Enstext dataset. Notably, on PosterErase, our unsupervised method achieves
5.07 in terms of FID, with a relative performance of 20.9% over existing
supervised baselines.

研究提出了一种新的自监督文本擦除 (STE) 框架，该框架通过联合学习合成带有擦除的训练图像和精确擦除现实世界中的文本来实现无监督场景文本擦除，提出了一种基于两个合成机制的样式感知图像合成函数，通过选择两个特别设计的奖励来控制合成机制，使用三元擦除损失促进精炼阶段恢复背景纹理，最后使用新的数据集 PosterErase 和广泛使用的 SCUT-Enstext 数据集进行了评估。

具有可控图像合成的自监督文字擦除

Self-Supervised Text Erasing with Controllable Image Synthesis

The character information in natural scene images contains various personal
information, such as telephone numbers, home addresses, etc. It is a high risk
of leakage the information if they are published. In this paper, we proposed a
scene text erasing method to properly hide the information via an inpainting
convolutional neural network (CNN) model. The input is a scene text image, and
the output is expected to be text erased image with all the character regions
filled up the colors of the surrounding background pixels. This work is
accomplished by a CNN model through convolution to deconvolution with
interconnection process. The training samples and the corresponding inpainting
images are considered as teaching signals for training. To evaluate the text
erasing performance, the output images are detected by a novel scene text
detection method. Subsequently, the same measurement on text detection is
utilized for testing the images in benchmark dataset ICDAR2013. Compared with
direct text detection way, the scene text erasing process demonstrates a
drastically decrease on the precision, recall and f-score. That proves the
effectiveness of proposed method for erasing the text in natural scene images.

本文提出了一种通过卷积神经网络模型擦除自然场景图像中文字信息的方法，并通过场景文本检测方法对其性能进行了测试，证明了与直接文本检测方式相比，场景文本擦除过程显着降低了精度、召回率和 F1 值。