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document understanding

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

Papers

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Showing 101–110 of 309 papers

TitleStatusHype
Shakti-VLMs: Scalable Vision-Language Models for Enterprise AI—0
OmniParser V2: Structured-Points-of-Thought for Unified Visual Text Parsing and Its Generality to Multimodal Large Language Models—0
KITAB-Bench: A Comprehensive Multi-Domain Benchmark for Arabic OCR and Document Understanding—0
Assessing Generative AI value in a public sector context: evidence from a field experiment—0
DocMIA: Document-Level Membership Inference Attacks against DocVQA ModelsCode0
HERITAGE: An End-to-End Web Platform for Processing Korean Historical Documents in HanjaCode0
BoundingDocs: a Unified Dataset for Document Question Answering with Spatial Annotations—0
Survey on Question Answering over Visually Rich Documents: Methods, Challenges, and Trends—0
Towards Natural Language-Based Document Image Retrieval: New Dataset and Benchmark—0
Zero-Shot Prompting and Few-Shot Fine-Tuning: Revisiting Document Image Classification Using Large Language Models—0
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