MinerU-Popo framework improves document parsing for RAG

Researchers have developed MinerU-Popo, a novel framework designed to enhance structured document parsing by addressing limitations in current VLM-based OCR models. This system focuses on reconstructing document-level logical structures, such as paragraphs and tables, that are often fragmented across page boundaries. By employing a lightweight post-processing model fine-tuned on a custom dataset and utilizing dynamic chunking for long documents, MinerU-Popo significantly improves accuracy in RAG applications and reduces latency. AI

影响 Enhances document understanding for AI systems, potentially improving RAG accuracy and efficiency.

排序理由 Publication of an academic paper detailing a new method for document parsing. [lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

• MinerU-Popo
• Qwen3-VL-4B
• VLM-based OCR models

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