This paper proposes a text removal model, Text Remove Network (TRNet), which achieves an unprecedented clearing effect of picture text. The network uses a jump-connected U-net structure to encode and decode the generator, so as to obtain clearer and sharper texture details of the original image. To solve the problem of color distortion, the generator removes the batch normalization layer and uses ELUs as the activation layer of all convolutional layers. Through the comprehensive loss, which include reconstruction, content, style, total variation, and structural similarity (SSIM) loss, we can clear the image text and preserve the image information of the background, which solves the problem of incomplete text removal and loss of background texture. The local discriminator is used to evaluate the local consistency of the text erasure area. In a text elimination experiment on a synthetic dataset and the ICDAR 2013 dataset, this method had a good effect on foreground text erasure and background authenticity restoration. Experiments on a comprehensive dataset of real documents also showed good results. To achieve targeted removal of sensitive text information on pictures, we collected datasets based on real and synthetic documents, and experimental results were satisfactory. Compared to current and classic algorithms, our text removal algorithm performs best in Image Quality Assessment (IQA).

本文提出了一种文本去除模型——Text Remove Network (TRNet)，实现了前所未有的图片文本清除效果。网络采用跳跃连接的U-net结构对生成器进行编码和解码，从而获得更清晰、更锐利的原始图像纹理细节。为了解决颜色失真的问题，生成器去除了批量归一化层，并使用 ELU 作为所有卷积层的激活层。通过综合损失，包括重构、内容、风格、总变异和结构相似性（SSIM）损失，我们可以清除图像文本并保留背景的图像信息，解决了文本删除不完整和丢失的问题。背景纹理。局部判别器用于评估文本擦除区域的局部一致性。在合成数据集和ICDAR 2013数据集的文本消除实验中，该方法对前景文本擦除和背景真实性恢复有很好的效果。在真实文档的综合数据集上的实验也显示出良好的结果。为了实现对图片敏感文本信息的针对性去除，我们收集了基于真实和合成文档的数据集，实验结果令人满意。与当前和经典算法相比，我们的文本去除算法在图像质量评估 (IQA) 中表现最佳。为了实现对图片敏感文本信息的针对性去除，我们收集了基于真实和合成文档的数据集，实验结果令人满意。与当前和经典算法相比，我们的文本去除算法在图像质量评估 (IQA) 中表现最佳。为了实现对图片敏感文本信息的针对性去除，我们收集了基于真实和合成文档的数据集，实验结果令人满意。与当前和经典算法相比，我们的文本去除算法在图像质量评估 (IQA) 中表现最佳。