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In this paper, we present StrucTexTv2, an effective document image pre-training framework, by performing masked visual-textual prediction.
Structext: Structured text understanding with multi-modal transformers
Yulin Li, Yuxi Qian, Yuechen Yu, Xiameng Qin, Chengquan Zhang, Yan Liu, Kun Yao, Junyu Han, Jingtuo Liu, and Errui Ding · 1920
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Building a test collection for complex document information processing
David D. Lewis, Gady Agam, Shlomo Argamon, Ophir Frieder, David A. Grossman, and Jefferson Heard · 2006
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Evaluation of deep convolutional nets for document image classification and retrieval
Adam W. Harley, Alex Ufkes, and Konstantinos G. Derpanis · 2015
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Adam: A method for stochastic optimization
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Mask R-CNN
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Towards end-to-end text spotting with convolutional recurrent neural networks
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Tsung-Yi Lin, Piotr Dollár, Ross B. Girshick, Kaiming He, Bharath Hariharan, and Serge J. Belongie · 2017
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Google cloud vision
Google · 2019
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FUNSD: A dataset for form understanding in noisy scanned documents
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Fast wordpiece tokenization
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Towards robust visual information extraction in real world: New dataset and novel solution
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Beit: BERT pre-training of image transformers
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End-to-end document recognition and understanding with dessurt
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Unified pretraining framework for document understanding
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Mask textspotter: An end-to-end trainable neural network for spotting text with arbitrary shapes
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Brian L. Davis, Bryan S. Morse, Brian L. Price, Chris Tensmeyer, Curtis Wigington, and Vlad I. Morariu · 2022
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Masked autoencoders are scalable vision learners
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Layoutlmv3: Pre-training for document AI with unified text and image masking
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Ocr-free document understanding transformer
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Dit: Self-supervised pre-training for document image transformer
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Simmim: a simple framework for masked image modeling
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XFUND: A benchmark dataset for multilingual visually rich form understanding
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Real-time scene text detection with differentiable binarization and adaptive scale fusion
Minghui Liao, Zhisheng Zou, Zhaoyi Wan, Cong Yao, and Xiang Bai · 2023
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Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing
Pengfei Liu, Weizhe Yuan, Jinlan Fu, Zhengbao Jiang, Hiroaki Hayashi, and Graham Neubig · 2023
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