toggle visibility Search & Display Options

Select All    Deselect All
 |   | 
  Record Links
Author Mohamed Ali Souibgui; Sanket Biswas; Sana Khamekhem Jemni; Yousri Kessentini; Alicia Fornes; Josep Llados; Umapada Pal edit   pdf
doi  openurl
  Title DocEnTr: An End-to-End Document Image Enhancement Transformer Type Conference Article
  Year 2022 Publication 26th International Conference on Pattern Recognition Abbreviated Journal  
  Volume Issue Pages 1699-1705  
  Keywords Degradation; Head; Optical character recognition; Self-supervised learning; Benchmark testing; Transformers; Magnetic heads  
  Abstract 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:  
  Address August 21-25, 2022 , Montréal Québec  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue (up) Edition  
  ISSN ISBN Medium  
  Area Expedition Conference ICPR  
  Notes DAG; 600.121; 600.162; 602.230; 600.140 Approved no  
  Call Number Admin @ si @ SBJ2022 Serial 3730  
Permanent link to this record
Select All    Deselect All
 |   | 

Save Citations:
Export Records: