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.