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Text-DIAE: A Self-Supervised Degradation Invariant Autoencoders for Text Recognition and Document Enhancement. (arXiv:2203.04814v4 [cs.CV] UPDATED)
Aug. 19, 2022, 1:12 a.m. | Mohamed Ali Souibgui, Sanket Biswas, Andres Mafla, Ali Furkan Biten, Alicia Fornés, Yousri Kessentini, Josep Lladós, Lluis Gomez, Dimostheni
cs.CV updates on arXiv.org arxiv.org
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) and document image enhancement. We
start by employing a transformer-based architecture that incorporates three
pretext tasks as learning objectives to be optimized during pre-training
without the usage of labeled data. Each of the pretext objectives is
specifically tailored for the final downstream tasks. We conduct several
ablation experiments that confirm the design choice of the selected …
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