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Pre-training of text and layout has proved effective in a variety of visually-rich document understanding tasks due to its effective model architecture and the advantage of large-scale unlabeled scanned/digital-born documents.
Modular multimodal architecture for document classification
Tyler Dauphinee, Nikunj Patel, and Mohammad Mehdi Rashidi. 2019 · 1912
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Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross B. Girshick. 2017 · 2017
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Saining Xie, Ross B. Girshick, Piotr Dollár, Zhuowen Tu, and Kaiming He. 2017 · 2017
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Xiao Yang, Ersin Yumer, Paul Asente, Mike Kraley, Daniel Kifer, and C. Lee Giles. 2017 · 2017
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Arindam Das, Saikat Roy, and Ujjwal Bhattacharya. 2018 · 2018
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Peter Shaw, Jakob Uszkoreit, and Ashish Vaswani. 2018 · 2018
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BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
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Icdar2019 competition on scanned receipt ocr and information extraction
Z. Huang, K. Chen, J. He, X. Bai, D. Karatzas, S. Lu, and C. V. Jawahar. 2019 · 2019
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Funsd: A dataset for form understanding in noisy scanned documents
Guillaume Jaume, Hazim Kemal Ekenel, and Jean-Philippe Thiran. 2019 · 2019
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Graph convolution for multimodal information extraction from visually rich documents
Xiaojing Liu, Feiyu Gao, Qiong Zhang, and Huasha Zhao. 2019 · 2019
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Ilya Loshchilov and Frank Hutter. 2019 · 2019
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Vilbert: Pretraining task-agnostic visiolinguistic representations for vision-and-language tasks
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Filip Graliński, Tomasz Stanisławek, Anna Wróblewska, Dawid Lipiński, Agnieszka Kaliska, Paulina Rosalska, Bartosz Topolski, and Przemysław Biecek. 2020 · 2020
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Deterministic routing between layout abstractions for multi-scale classification of visually rich documents
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