Document Image Binarization is a well-known problem in Document Analysis and Computer Vision, although it is far from being solved. One of the main challenges of this task is that documents generally exhibit degradations and acquisition artifacts that can greatly vary throughout the page. Nonetheless, even when dealing with a local patch of the document, taking into account the overall appearance of a wide portion of the page can ease the prediction by enriching it with semantic information on the ink and background conditions. In this respect, approaches able to model both local and global information have been proven suitable for this task. In particular, recent applications of Vision Transformer (ViT)-based models, able to model short and long-range dependencies via the attention mechanism, have demonstrated their superiority over standard Convolution-based models, which instead struggle to model global dependencies. In this work, we propose an alternative solution based on the recently introduced Fast Fourier Convolutions, which overcomes the limitation of standard convolutions in modeling global information while requiring fewer parameters than ViTs. We validate the effectiveness of our approach via extensive experimental analysis considering different types of degradations.

翻译：文档图像二值化是文档分析与计算机视觉领域的经典问题，但尚未得到完全解决。该任务的主要挑战之一在于，文档通常存在退化及采集伪影，且这些特征在全页范围内变化显著。然而，即便针对文档局部区域进行处理时，综合考虑页面大范围区域的整体外观特征，仍可通过补充墨迹与背景状态的语义信息来优化预测结果。为此，能够同时建模局部与全局信息的技术方案已被证明适用于该任务。值得注意的是，近期基于视觉Transformer（ViT）的模型通过注意力机制实现短程与长程依赖建模，已展现出优于传统卷积模型的性能，而后者在全局依赖建模方面存在局限。本研究提出基于最近引入的快速傅里叶卷积的替代方案，该方案在克服标准卷积全局信息建模局限性的同时，参数数量少于ViT。通过考虑不同类型退化的广泛实验分析，我们验证了所提方法的有效性。