论文标题
Shadocnet：在变压器中学习空间意识令牌以删除文档阴影
ShaDocNet: Learning Spatial-Aware Tokens in Transformer for Document Shadow Removal
论文作者
论文摘要
阴影去除可提高文档数字副本的视觉质量和可读性。但是，文档删除仍然是一个尚未解决的主题。传统技术取决于启发式方法，这些启发式方法因情况而异。鉴于当前公共数据集的质量和数量，大多数神经网络模型都无法完成此任务。在本文中，我们提出了一个基于变压器的模型，用于删除文档阴影，该模型利用阴影和无阴影区域中编码和解码的阴影上下文。此外，整个粗到精细的过程中包括阴影检测和像素级增强。根据全面的基准评估，它与最先进的方法具有竞争力。
Shadow removal improves the visual quality and legibility of digital copies of documents. However, document shadow removal remains an unresolved subject. Traditional techniques rely on heuristics that vary from situation to situation. Given the quality and quantity of current public datasets, the majority of neural network models are ill-equipped for this task. In this paper, we propose a Transformer-based model for document shadow removal that utilizes shadow context encoding and decoding in both shadow and shadow-free regions. Additionally, shadow detection and pixel-level enhancement are included in the whole coarse-to-fine process. On the basis of comprehensive benchmark evaluations, it is competitive with state-of-the-art methods.
