PT Unknown
AU Mohamed Ali Souibgui
   Sanket Biswas
   Sana Khamekhem Jemni
   Yousri Kessentini
   Alicia Fornes
   Josep Llados
   Umapada Pal
TI DocEnTr: An End-to-End Document Image Enhancement Transformer
BT 26th International Conference on Pattern Recognition
PY 2022
BP 1699
EP 1705
DI 10.1109/ICPR56361.2022.9956101
DE Degradation; Head; Optical character recognition; Self-supervised learning; Benchmark testing; Transformers; Magnetic heads
AB 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: https://github.com/dali92002/DocEnTR
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