Methods › Natural Language Processing › Document Understanding Models › LayoutLMv2
LayoutLMv2
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
LayoutLMv2 is an architecture and pre-training method for document understanding. The model is pre-trained with a great number of unlabeled scanned document images from the IIT-CDIP dataset, where some images in the text-image pairs are randomly replaced with another document image to make the model learn whether the image and OCR texts are correlated or not. Meanwhile, it also integrates a spatial-aware self-attention mechanism into the Transformer architecture, so that the model can fully understand the relative positional relationship among different text blocks.
Specifically, an enhanced Transformer architecture is used, i.e. a multi-modal Transformer asisthe backbone of LayoutLMv2. The multi-modal Transformer accepts inputs of three modalities: text, image, and layout. The input of each modality is converted to an embedding sequence and fused by the encoder. The model establishes deep interactions within and between modalities by leveraging the powerful Transformer layers.
Papers archive 2025-07-28
3 shown of 3, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
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PEneo: Unifying Line Extraction, Line Grouping, and Entity Linking for End-to-end Document Pair Extraction 7 Jan 2024 · 1 repository · arXiv:2401.03472
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ProtoNER: Few shot Incremental Learning for Named Entity Recognition using Prototypical Networks 3 Oct 2023 · 0 repositories · arXiv:2310.02372
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LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding 29 Dec 2020 · 9 repositories · arXiv:2012.14740
Tasks archive 2025-07-28
18 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
Usage over time archive 2025-07-28
Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).
Categories archive 2025-07-28
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