Document image enhancement is a fundamental and important stage for attaining
the best performance in any document analysis assignment because there are many
degradation situations that could harm document images, making it more
difficult to recognize and analyze them. In this paper, we propose
\textbf{T2T-BinFormer} which is a novel document binarization encoder-decoder
architecture based on a Tokens-to-token vision transformer. Each image is
divided into a set of tokens with a defined length using the ViT model, which
is then applied several times to model the global relationship between the
tokens. However, the conventional tokenization of input data does not
adequately reflect the crucial local structure between adjacent pixels of the
input image, which results in low efficiency. Instead of using a simple ViT and
hard splitting of images for the document image enhancement task, we employed a
progressive tokenization technique to capture this local information from an
image to achieve more effective results. Experiments on various DIBCO and
H-DIBCO benchmarks demonstrate that the proposed model outperforms the existing
CNN and ViT-based state-of-the-art methods. In this research, the primary area
of examination is the application of the proposed architecture to the task of
document binarization. The source code will be made available at
this https URL

提出了一种基于 Tokens-to-token 视觉变换器的新型文档二值化编码 - 解码器架构 T2T-BinFormer，通过逐步分词技术捕捉图像的局部信息，优于现有的 CNN 和 ViT 方法，在各个基准测试中表现出更高的效果。