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document understanding

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

Papers

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Showing 291–300 of 309 papers

TitleStatusHype
MMDocBench: Benchmarking Large Vision-Language Models for Fine-Grained Visual Document Understanding—0
mPLUG-DocOwl 1.5: Unified Structure Learning for OCR-free Document Understanding—0
mPLUG-DocOwl2: High-resolution Compressing for OCR-free Multi-page Document Understanding—0
mPLUG-DocOwl: Modularized Multimodal Large Language Model for Document Understanding—0
MT^3: Scaling MLLM-based Text Image Machine Translation via Multi-Task Reinforcement Learning—0
Multi-modal Information Extraction from Text, Semi-structured, and Tabular Data on the Web—0
NAMER: Non-Autoregressive Modeling for Handwritten Mathematical Expression Recognition—0
NoTeS-Bank: Benchmarking Neural Transcription and Search for Scientific Notes Understanding—0
Notes on Applicability of GPT-4 to Document Understanding—0
Object-oriented Neural Programming (OONP) for Document Understanding—0
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