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document understanding

Document understanding involves document classification, layout analysis, information extraction, and DocQA.

Papers

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Showing 61–70 of 309 papers

TitleStatusHype
DocLayLLM: An Efficient Multi-modal Extension of Large Language Models for Text-rich Document UnderstandingCode1
Value Retrieval with Arbitrary Queries for Form-like DocumentsCode1
Doc2Graph: a Task Agnostic Document Understanding Framework based on Graph Neural NetworksCode1
DocTrack: A Visually-Rich Document Dataset Really Aligned with Human Eye Movement for Machine ReadingCode1
DocLayLLM: An Efficient and Effective Multi-modal Extension of Large Language Models for Text-rich Document UnderstandingCode1
Leveraging Visual Tokens for Extended Text Contexts in Multi-Modal LearningCode1
LineFormer: Rethinking Line Chart Data Extraction as Instance SegmentationCode1
DocFormer: End-to-End Transformer for Document UnderstandingCode1
DocFormerv2: Local Features for Document UnderstandingCode1
LayoutLLM: Layout Instruction Tuning with Large Language Models for Document UnderstandingCode0
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