  Document images can be affected by many degradation scenarios, which cause
recognition and processing difficulties. In this age of digitization, it is
important to denoise them for proper usage. To address this challenge, we
present a new encoder-decoder architecture based on vision transformers to
enhance both machine-printed and handwritten document images, in an end-to-end
fashion. The encoder operates directly on the pixel patches with their
positional information without the use of any convolutional layers, while the
decoder reconstructs a clean image from the encoded patches. Conducted
experiments show a superiority of the proposed model compared to the state-of
the-art methods on several DIBCO benchmarks. Code and models will be publicly
available at: \url{https://github.com/dali92002/DocEnTR}.
