  The rapid evolution of intelligent document processing systems demands robust
solutions that adapt to diverse domains without extensive retraining.
Traditional methods often falter with variable document types, leading to poor
performance. To overcome these limitations, this paper introduces a
text-graphic layer separation approach that enhances domain adaptability in
document image restoration (DIR) systems. We propose LayeredDoc, which utilizes
two layers of information: the first targets coarse-grained graphic components,
while the second refines machine-printed textual content. This hierarchical DIR
framework dynamically adjusts to the characteristics of the input document,
facilitating effective domain adaptation. We evaluated our approach both
qualitatively and quantitatively using a new real-world dataset, LayeredDocDB,
developed for this study. Initially trained on a synthetically generated
dataset, our model demonstrates strong generalization capabilities for the DIR
task, offering a promising solution for handling variability in real-world
data. Our code is accessible on GitHub.
