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Document image enhancement is a fundamental and important stage for attainingthe best performance in any document analysis assignment because there are manydegradation situations that could harm document images, making it moredifficult to recognize and analyze them. In this paper, we propose\textbf{T2T-BinFormer} which is a novel document binarization encoder-decoderarchitecture based on a Tokens-to-token vision transformer. Each image isdivided into a set of tokens with a defined length using the ViT model, whichis then applied several times to model the global relationship between thetokens. However, the conventional tokenization of input data does notadequately reflect the crucial local structure between adjacent pixels of theinput image, which results in low efficiency. Instead of using a simple ViT andhard splitting of images for the document image enhancement task, we employed aprogressive tokenization technique to capture this local information from animage to achieve more effective results. Experiments on various DIBCO andH-DIBCO benchmarks demonstrate that the proposed model outperforms the existingCNN and ViT-based state-of-the-art methods. In this research, the primary areaof examination is the application of the proposed architecture to the task ofdocument binarization. The source code will be made available athttps://github.com/RisabBiswas/T2T-BinFormer.

A Layer-Wise Tokens-to-Token Transformer Network for Improved Historical Document Image Enhancement