MinerU-Popo: Universal Post-Processing Model for Structured Document Parsing
📰 ArXiv cs.AI
Learn how MinerU-Popo improves document parsing by post-processing VLM-based OCR models to recover disrupted structures and ensure document-level coherence
Action Steps
- Implement VLM-based OCR models for page-level element extraction
- Apply MinerU-Popo post-processing to recover disrupted structures and ensure document-level coherence
- Evaluate the performance of MinerU-Popo using metrics such as accuracy and F1-score
- Fine-tune MinerU-Popo hyperparameters to optimize its performance on specific document parsing tasks
- Integrate MinerU-Popo with downstream applications such as RAG to leverage its improved document-level information
Who Needs to Know This
NLP engineers and researchers working on document parsing and information extraction tasks can benefit from this knowledge to improve their models' performance and accuracy
Key Insight
💡 MinerU-Popo can significantly improve the accuracy and coherence of document parsing by post-processing VLM-based OCR models
Share This
📄 Improve document parsing with MinerU-Popo, a universal post-processing model for recovering disrupted structures and ensuring document-level coherence 💡
Full Article
Title: MinerU-Popo: Universal Post-Processing Model for Structured Document Parsing
Abstract:
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. However, downstream applications such as RAG require coherent document-level information, whereas these models often break cross-page continuity and fail to recover disrupted structures, such as paragraphs and tables truncate
Abstract:
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. However, downstream applications such as RAG require coherent document-level information, whereas these models often break cross-page continuity and fail to recover disrupted structures, such as paragraphs and tables truncate
DeepCamp AI