工业场景下高斯引导的非显著性字符抹除

Gaussian-guided Non-saliency Character Erasure under Industrial Scenarios

图像文本信息在日常生活中无处不在,在传递信息的同时,也带来了信息泄露的问题.近年来文本擦除模型很好地解决了这个问题.然而,在工业场景下,图像会出现高光,对比度较大的非字符区域,模型往往很容易其影响发生注意力偏移的现象,从而忽略了字符区域导致不理想的文本抹除效果.为了克服这一局限性,基于注意力提出了一种新的文本擦除网络,即在网络中嵌入了一层额外的特征层用以给生成图中存在字符的区域进行评分.同时,引入了高斯热力图并将其作为基础设计损失函数,采用监督的方式纠正模型的注意力,将模型注意力引导至正确的字符区域.通过在4种不同的数据集上进行对比,本文所提方法总体上拥有更好的抹除效果.同时,该方法在图像存在复杂的背景情况下,其在图像抹除任务中仍然具有较高的灵活性.

Image text messages are ubiquitous in everyday life,and while conveying information,they also bring the problem of information leakage.In recent years,text erasure models have solved this problem very well.However,in industrial scenarios where images are highlighted and non-character areas with high contrast,the models are often susceptible to their influence of attentional drift,thus neglecting the character areas and resulting in unsatisfactory text erasure.In order to overcome this limitation,this study proposes a new text erasure network based on attention.Specifically,an additional feature layer is embedded in the network to score the areas where characters are present in the generated image.At the same time,the study introduces a Gaussian heat map and uses it as the basis for designing a loss function that corrects the model’s attention and guides it to accurate character areas in a supervised manner.Through comparison on four different datasets,the proposed method has better erasure results overall.In addition,the method has the same high flexibility for the text erasure task in the presence of complex backgrounds in images.