Multi-modal Document Understanding
Multi-Modal Document Understanding is a document analysis framework that integrates and interprets complementary information from multiple data modalities—such as textual, visual, and layout features—within a single document to achieve a comprehensive, context-aware representation of its content and structure.
33
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2.3K
Citations
174
Authors
35
Institutions
Publications per year
2017–2026
30
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LayoutLMv2: Multi-modal Pre-training for Visually-rich Document Understanding
Yang Xu, Yiheng Xu, Tengchao Lv et al. · 2021 · 438 citations · Full text
DocBank: A Benchmark Dataset for Document Layout Analysis
Minghao Li, Yiheng Xu, Lei Cui et al. · 2020 · 186 citations · Full text
StrucTexT: Structured Text Understanding with Multi-Modal Transformers
Yulin Li, Yuxi Qian, Yuechen Yu et al. · 2021 · 118 citations
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Microsoft Research (United Kingdom)
Cambridge, United Kingdom
3 h-index
22 publications
1K citations
Microsoft Research Asia (China)
Beijing, China
3 h-index
14 publications
3.6K citations
University of Science and Technology of China
Hefei, China
3 h-index
7 publications
209 citations
Microsoft (United States)
Redmond, United States
2 h-index
12 publications
2.5K citations

Shanghai Jiao Tong University
Shanghai, China
2 h-index
3 publications
194 citations
Baidu (China)
Beijing, China
2 h-index
16 publications
1.1K citations

Beihang University
Beijing, China
2 h-index
4 publications
416 citations
University of Chinese Academy of Sciences
Beijing, China
2 h-index
3 publications
105 citations
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Repository · 9 publications · 236 citations
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Conference · 2 publications · 47 citations
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2 publications · 86 citations
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Conference · 1 publications · 28 citations
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Conference · 1 publications · 45 citations
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