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Author (up) Mohamed Ali Souibgui; Sanket Biswas; Sana Khamekhem Jemni; Yousri Kessentini; Alicia Fornes; Josep Llados; Umapada Pal
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: https://github.com/dali92002/DocEnTR
Address August 21-25, 2022 , Montréal Québec
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Notes DAG; 600.121; 600.162; 602.230; 600.140 Approved no
Call Number Admin @ si @ SBJ2022 Serial 3730
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