DiT: Self-supervised Pre-training for Document Image Transformer

Image Transformer has recently achieved significant progress for natural image understanding, either using supervised (ViT, DeiT, etc.) or self-supervised (BEiT, MAE, etc.) pre-training techniques. In this paper, we propose \textbf{DiT}, a self-supervised pre-trained \textbf{D}ocument \textbf{I}mage...

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Bibliographic Details
Published in:arXiv.org 2022-07
Main Authors: Li, Junlong, Xu, Yiheng, Lv, Tengchao, Cui, Lei, Zhang, Cha, Furu Wei
Format: Article
Language:English
Subjects:
Computer networks
Documents
Downstream effects
Image classification
Layouts
Training
Online Access:Get full text
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Summary:Image Transformer has recently achieved significant progress for natural image understanding, either using supervised (ViT, DeiT, etc.) or self-supervised (BEiT, MAE, etc.) pre-training techniques. In this paper, we propose \textbf{DiT}, a self-supervised pre-trained \textbf{D}ocument \textbf{I}mage \textbf{T}ransformer model using large-scale unlabeled text images for Document AI tasks, which is essential since no supervised counterparts ever exist due to the lack of human-labeled document images. We leverage DiT as the backbone network in a variety of vision-based Document AI tasks, including document image classification, document layout analysis, table detection as well as text detection for OCR. Experiment results have illustrated that the self-supervised pre-trained DiT model achieves new state-of-the-art results on these downstream tasks, e.g. document image classification (91.11 \(\rightarrow\) 92.69), document layout analysis (91.0 \(\rightarrow\) 94.9), table detection (94.23 \(\rightarrow\) 96.55) and text detection for OCR (93.07 \(\rightarrow\) 94.29). The code and pre-trained models are publicly available at \url{https://aka.ms/msdit}.
ISSN:2331-8422