MinerU: An Open-Source Solution for Precise Document Content Extraction (2409.18839v1)

Abstract: Document content analysis has been a crucial research area in computer vision. Despite significant advancements in methods such as OCR, layout detection, and formula recognition, existing open-source solutions struggle to consistently deliver high-quality content extraction due to the diversity in document types and content. To address these challenges, we present MinerU, an open-source solution for high-precision document content extraction. MinerU leverages the sophisticated PDF-Extract-Kit models to extract content from diverse documents effectively and employs finely-tuned preprocessing and postprocessing rules to ensure the accuracy of the final results. Experimental results demonstrate that MinerU consistently achieves high performance across various document types, significantly enhancing the quality and consistency of content extraction. The MinerU open-source project is available at https://github.com/opendatalab/MinerU.

• The paper introduces MinerU, a multi-module parsing solution that significantly enhances document content extraction precision.
• The paper leverages specialized models, including UniMERNet and YOLO-based systems, to deliver robust layout and formula recognition.
• The paper highlights MinerU's practical impact as an open-source project that accelerates research and improves document analysis workflows.

MinerU: An Open-Source Solution for Precise Document Content Extraction

The paper "MinerU: An Open-Source Solution for Precise Document Content Extraction," authored by researchers from the Shanghai Artificial Intelligence Laboratory, presents an advanced toolchain for document content extraction. This toolchain, named MinerU, promises significant improvements in extracting high-quality content from a wide variety of documents. The authors articulate the challenges in document content analysis and propose MinerU as an innovative solution leveraging models from the PDF-Extract-Kit, combined with sophisticated preprocessing and post-processing rules.

Technical Approaches and Core Components

The primary technical approach in MinerU is multi-module document parsing. This strategy dissects documents into different regions, such as text blocks, tables, and formulas, each processed by specialized recognition models. The core components of MinerU include:

• Layout Detection: Utilizing fine-tuned models, MinerU identifies distinct document regions with high precision, including titles, paragraphs, images, and tables.
• Formula Detection: Dedicated models recognize inline and displayed formulas, crucial for maintaining the integrity of scientific documents.
• OCR: Integrated OCR processes text within identified regions, ensuring the reading order is preserved.
• Table Recognition: By leveraging models such as TableMaster and StructEqTable, MinerU effectively processes complex tables from diverse documents.
• Formula Recognition: The UniMERNet model, trained on the UniMER-1M dataset, excels in recognizing a variety of formulas, outperforming other open-source and some commercial solutions.

Framework Workflow

MinerU's workflow is divided into four stages:

1. Document Preprocessing: Filtering out non-processable PDFs and extracting metadata (e.g., language type, page dimensions).
2. Document Content Parsing: Using the PDF-Extract-Kit for layout analysis, formula detection, OCR, and table recognition.
3. Document Content Post-Processing: Adjusting content order by resolving overlaps and stitching segmented content according to human reading sequences.
4. Format Conversion: Converting processed content into machine-readable formats, such as Markdown or JSON, to meet user needs.

Evaluation and Results

The paper presents a methodical evaluation of MinerU, comparing its performance against state-of-the-art open-source models. The evaluation encompasses:

• Layout Detection: MinerU's layout detection models showed superior performance in terms of mAP and AR50 across diverse datasets, significantly outperforming competitive models like DocXchain and LayoutLMv3.
• Formula Detection: MinerU's YOLO-based models surpassed Pix2Text-MFD, demonstrating higher accuracy and robustness to varied document types.
• Formula Recognition: The UniMERNet model exhibited strong results on the UniMER-Test dataset, with high CDM scores indicative of robust performance across different formula representations.

Implications and Future Directions

MinerU's ability to handle diverse document types, including academic papers, textbooks, and financial reports, suggests wide-ranging practical and theoretical implications. The tool's adaptability enhances the quality and consistency of content extraction, potentially accelerating research and development in fields relying heavily on document analysis. The availability of MinerU as an open-source project (https://github.com/opendatalab/MinerU) further facilitates community contributions and integrations into existing workflows.

Future developments envisioned by the authors include:

1. Core Component Enhancement: Continued iteration on current models and introduction of new models to refine document extraction capabilities.
2. Usability and Speed Optimization: Improvements in the processing pipeline to boost speed and user experience, alongside efficient online inference services.
3. Benchmark Construction: Establishing a systematic evaluation benchmark for diverse documents to provide clear comparisons with other state-of-the-art methods.

Conclusion

The MinerU tool represents a significant advancement in the domain of document content extraction. By leveraging sophisticated model ensembles and tailored workflows, MinerU addresses the diverse nature of document structures with high precision. The paper's detailed evaluations affirm MinerU's capability to substantially elevate the quality and consistency of extracted content, setting a new standard for open-source solutions in this critical research area.