OCRBench v2 is a new benchmark with four times more tasks than prior versions that reveals most large multimodal models score below 50 out of 100 on visual text tasks and share five specific weaknesses.
mplug- docowl2: High-resolution compressing for ocr-free multi-page document understanding
9 Pith papers cite this work. Polarity classification is still indexing.
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VisRAG achieves 20-40% better end-to-end performance than text-based RAG by directly embedding and retrieving document images with VLMs.
A realistic scene synthesis strategy and document-aware training recipe enable a 1B-parameter MLLM to achieve superior accuracy and robustness in end-to-end parsing of real-world captured documents.
MoDora introduces local-alignment aggregation, a Component-Correlation Tree, and question-type-aware retrieval to improve accuracy on semi-structured document QA by 5.97-61.07% over baselines.
RPC-Bench supplies 15K verified QA pairs and a research-flow taxonomy that shows top foundation models still achieve only 68.2 percent correctness-completeness on academic paper comprehension.
Unveil proposes a visual-textual embedding model for multi-modal documents that is distilled into an efficient visual-only retriever.
MinerU delivers an open-source pipeline for high-precision document content extraction by integrating specialized models with tuned preprocessing and postprocessing rules.
A survey of MLLM-based Visually Rich Document Understanding covering feature integration techniques, training paradigms, challenges like data scarcity, and emerging trends such as RAG and agentic frameworks.
Survey proposing a taxonomy for document parsing into pipeline-based systems and VLM-driven unified models, reviewing components, metrics, benchmarks, and challenges.
citing papers explorer
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OCRBench v2: An Improved Benchmark for Evaluating Large Multimodal Models on Visual Text Localization and Reasoning
OCRBench v2 is a new benchmark with four times more tasks than prior versions that reveals most large multimodal models score below 50 out of 100 on visual text tasks and share five specific weaknesses.
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VisRAG: Vision-based Retrieval-augmented Generation on Multi-modality Documents
VisRAG achieves 20-40% better end-to-end performance than text-based RAG by directly embedding and retrieving document images with VLMs.
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Towards Real-World Document Parsing via Realistic Scene Synthesis and Document-Aware Training
A realistic scene synthesis strategy and document-aware training recipe enable a 1B-parameter MLLM to achieve superior accuracy and robustness in end-to-end parsing of real-world captured documents.
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MoDora: Tree-Based Semi-Structured Document Analysis System
MoDora introduces local-alignment aggregation, a Component-Correlation Tree, and question-type-aware retrieval to improve accuracy on semi-structured document QA by 5.97-61.07% over baselines.
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RPC-Bench: A Fine-grained Benchmark for Research Paper Comprehension
RPC-Bench supplies 15K verified QA pairs and a research-flow taxonomy that shows top foundation models still achieve only 68.2 percent correctness-completeness on academic paper comprehension.
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Unveil: Unified Visual-Textual Integration and Distillation for Multi-modal Document Retrieval
Unveil proposes a visual-textual embedding model for multi-modal documents that is distilled into an efficient visual-only retriever.
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MinerU: An Open-Source Solution for Precise Document Content Extraction
MinerU delivers an open-source pipeline for high-precision document content extraction by integrating specialized models with tuned preprocessing and postprocessing rules.
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A Survey on MLLM-based Visually Rich Document Understanding: Methods, Challenges, and Emerging Trends
A survey of MLLM-based Visually Rich Document Understanding covering feature integration techniques, training paradigms, challenges like data scarcity, and emerging trends such as RAG and agentic frameworks.
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Document Parsing Unveiled: Techniques, Challenges, and Prospects for Structured Information Extraction
Survey proposing a taxonomy for document parsing into pipeline-based systems and VLM-driven unified models, reviewing components, metrics, benchmarks, and challenges.