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document understanding

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

Papers

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Showing 241–250 of 309 papers

TitleStatusHype
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
OmniParser: A Unified Framework for Text Spotting, Key Information Extraction and Table Recognition—0
OmniParser: A Unified Framework for Text Spotting Key Information Extraction and Table Recognition—0
OmniParser V2: Structured-Points-of-Thought for Unified Visual Text Parsing and Its Generality to Multimodal Large Language Models—0
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