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document understanding

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

Papers

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Showing 76–100 of 309 papers

TitleStatusHype
Automatic Knowledge Extraction with Human Interface—0
Decontextualization: Making Sentences Stand-Alone—0
Arctic-TILT. Business Document Understanding at Sub-Billion Scale—0
DAViD: Domain Adaptive Visually-Rich Document Understanding with Synthetic Insights—0
Automated Parsing of Engineering Drawings for Structured Information Extraction Using a Fine-tuned Document Understanding Transformer—0
Finding Pragmatic Differences Between Disciplines—0
Auto-encodeurs pour la compr\'ehension de documents parl\'es (Auto-encoders for Spoken Document Understanding)—0
A User-Centered Concept Mining System for Query and Document Understanding at Tencent—0
CREPE: Coordinate-Aware End-to-End Document Parser—0
WildDoc: How Far Are We from Achieving Comprehensive and Robust Document Understanding in the Wild?—0
ClueWeb22: 10 Billion Web Documents with Visual and Semantic Information—0
Attention Where It Matters: Rethinking Visual Document Understanding with Selective Region Concentration—0
DrVideo: Document Retrieval Based Long Video Understanding—0
Attention-Based Graph Neural Network with Global Context Awareness for Document Understanding—0
Acronym Identification and Disambiguation Shared Tasks for Scientific Document Understanding—0
Extract with Order for Coherent Multi-Document Summarization—0
Fast-StrucTexT: An Efficient Hourglass Transformer with Modality-guided Dynamic Token Merge for Document Understanding—0
FormNet: Structural Encoding beyond Sequential Modeling in Form Document Information Extraction—0
A Multi-Modal Multilingual Benchmark for Document Image Classification—0
Enhancing Visual Document Understanding with Contrastive Learning in Large Visual-Language Models—0
DONUT-hole: DONUT Sparsification by Harnessing Knowledge and Optimizing Learning Efficiency—0
DOGE: Towards Versatile Visual Document Grounding and Referring—0
DUBLIN -- Document Understanding By Language-Image Network—0
Efficient End-to-End Visual Document Understanding with Rationale Distillation—0
A Token-level Text Image Foundation Model for Document Understanding—0
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