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。

用于改进历史文献图像增强的逐层令牌到令牌转换网络

A Layer-Wise Tokens-to-Token Transformer Network for Improved Historical  Document Image Enhancement

In real life, various degradation scenarios exist that might damage document
images, making it harder to recognize and analyze them, thus binarization is a
fundamental and crucial step for achieving the most optimal performance in any
document analysis task. We propose DocBinFormer (Document Binarization
Transformer), a novel two-level vision transformer (TL-ViT) architecture based
on vision transformers for effective document image binarization. The presented
architecture employs a two-level transformer encoder to effectively capture
both global and local feature representation from the input images. These
complimentary bi-level features are exploited for efficient document image
binarization, resulting in improved results for system-generated as well as
handwritten document images in a comprehensive approach. With the absence of
convolutional layers, the transformer encoder uses the pixel patches and
sub-patches along with their positional information to operate directly on
them, while the decoder generates a clean (binarized) output image from the
latent representation of the patches. Instead of using a simple vision
transformer block to extract information from the image patches, the proposed
architecture uses two transformer blocks for greater coverage of the extracted
feature space on a global and local scale. The encoded feature representation
is used by the decoder block to generate the corresponding binarized output.
Extensive experiments on a variety of DIBCO and H-DIBCO benchmarks show that
the proposed model outperforms state-of-the-art techniques on four metrics. The
source code will be made available at
this https URL

提出了一种名为 DocBinFormer（文档二值化变换器）的基于视觉变换器的两级架构，用于有效地对文档图像进行二值化分析，该架构利用双级变换器编码器从输入图像中有效地捕获全局和局部特征表示，从而在系统生成和手写文档图像的分析中取得了改进的结果，并在 DIBCO 和 H-DIBCO 基准数据集上超过了现有技术的表现。