The Vision Transformer (ViT) architecture has been remarkably successful in image
restoration. For a while, Convolutional Neural Networks (CNN) predominated in most …

Document image enhancement methods are often used to improve the accuracy and
efficiency of automated document analysis and recognition tasks such as character …

Removing degradation from document images not only improves their visual quality and
readability, but also enhances the performance of numerous automated document analysis …

Many problems can reduce handwritten character recognition performance, such as image
degradation, light conditions, low-resolution images, and even the quality of the capture …

Premise Quantitative plant traits play a crucial role in biological research. However,
traditional methods for measuring plant morphology are time consuming and have limited …

Pretraining has proven successful in Document Intelligence tasks where deluge of
documents are used to pretrain the models only later to be finetuned on downstream tasks …

Document images are often degraded by various stains, significantly impacting their
readability and hindering downstream applications such as document digitization and …

In this paper, we propose a Text-Degradation Invariant Auto Encoder (Text-DIAE), a self-
supervised model designed to tackle two tasks, text recognition (handwritten or scene-text) …

Document binarization is a crucial pre-processing step for various document analysis tasks.
However, existing methods fail to accurately capture stroke edges, primarily due to the …

Due to the light scattering and absorption of impurities, the quality of underwater imaging is
poor, which seriously affects underwater exploration and research. To address the problem …