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document understanding

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

Papers

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Showing 176–200 of 309 papers

TitleStatusHype
"What is the value of templates?" Rethinking Document Information Extraction Datasets for LLMs—0
What Makes a Good Dataset for Symbol Description Reading?—0
WikiMixQA: A Multimodal Benchmark for Question Answering over Tables and Charts—0
Workshop on Document Intelligence Understanding—0
XFUND: A Benchmark Dataset for Multilingual Visually Rich Form Understanding—0
Deep Learning based Visually Rich Document Content Understanding: A Survey—0
Zero-Shot Prompting and Few-Shot Fine-Tuning: Revisiting Document Image Classification Using Large Language Models—0
WildDoc: How Far Are We from Achieving Comprehensive and Robust Document Understanding in the Wild?—0
VRDU: A Benchmark for Visually-rich Document Understanding—0
Acronym Identification and Disambiguation Shared Tasks for Scientific Document Understanding—0
A LayoutLMv3-Based Model for Enhanced Relation Extraction in Visually-Rich Documents—0
A Multi-Modal Multilingual Benchmark for Document Image Classification—0
Arctic-TILT. Business Document Understanding at Sub-Billion Scale—0
A Retrospective Recount of Computer Architecture Research with a Data-Driven Study of Over Four Decades of ISCA Publications—0
A Simple yet Effective Layout Token in Large Language Models for Document Understanding—0
Assessing Generative AI value in a public sector context: evidence from a field experiment—0
A Survey and Approach to Chart Classification—0
A Survey on MLLM-based Visually Rich Document Understanding: Methods, Challenges, and Emerging Trends—0
A Survey on Vietnamese Document Analysis and Recognition: Challenges and Future Directions—0
AT-BERT: Adversarial Training BERT for Acronym Identification Winning Solution for SDU@AAAI-21—0
A Token-level Text Image Foundation Model for Document Understanding—0
Attention-Based Graph Neural Network with Global Context Awareness for Document Understanding—0
Attention Where It Matters: Rethinking Visual Document Understanding with Selective Region Concentration—0
A User-Centered Concept Mining System for Query and Document Understanding at Tencent—0
Auto-encodeurs pour la compr\'ehension de documents parl\'es (Auto-encoders for Spoken Document Understanding)—0
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