Text-DIAE: Degradation Invariant Autoencoders for Text Recognition and Document Enhancement

• URL: http://arxiv.org/abs/2203.04814v2
• Date: Thu, 10 Mar 2022 17:39:02 GMT
• Title: Text-DIAE: Degradation Invariant Autoencoders for Text Recognition and Document Enhancement
• Authors: Mohamed Ali Souibgui, Sanket Biswas, Andres Mafla, Ali Furkan Biten, Alicia Forn\'es, Yousri Kessentini, Josep Llad\'os, Lluis Gomez, Dimosthenis Karatzas
• Abstract summary: Text-DIAE aims to solve two tasks, text recognition (handwritten or scene-text) and document image enhancement. We define three pretext tasks as learning objectives to be optimized during pre-training without the usage of labelled data. Our method surpasses the state-of-the-art significantly in existing supervised and self-supervised settings.
• Score: 8.428866479825736
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this work, we propose Text-Degradation Invariant Auto Encoder (Text-DIAE) aimed to solve two tasks, text recognition (handwritten or scene-text) and document image enhancement. We define three pretext tasks as learning objectives to be optimized during pre-training without the usage of labelled data. Each of the pre-text objectives is specifically tailored for the final downstream tasks. We conduct several ablation experiments that show the importance of each degradation for a specific domain. Exhaustive experimentation shows that our method does not have limitations of previous state-of-the-art based on contrastive losses while at the same time requiring essentially fewer data samples to converge. Finally, we demonstrate that our method surpasses the state-of-the-art significantly in existing supervised and self-supervised settings in handwritten and scene text recognition and document image enhancement. Our code and trained models will be made publicly available at~\url{ http://Upon_Acceptance}.

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