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MinerU-Popo framework improves document parsing for RAG

By PulseAugur Editorial · [1 sources] ·

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

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

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

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

MinerU-Popo framework improves document parsing for RAG

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

  1. arXiv cs.AI TIER_1 English(EN) · Bangrui Xu, Ziyang Miao, Xuanhe Zhou, Yiming Lin, Zirui Tang, Xiaomeng Zhao, Fan Wu, Cheng Tan, Fan Wu, Bin Wang, Conghui He ·

    MinerU-Popo: Universal Post-Processing Model for Structured Document Parsing

    arXiv:2605.24973v1 Announce Type: cross Abstract: VLM-based OCR models have become the de facto choice for document parsing, as they can accurately extract page-level elements (e.g., paragraphs within individual pages) together with their bounding boxes and textual content. Howev…

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