RAG knowledge base essential document extraction open source projects comparison

Recently, I've been choosing a smart customer service program for RAG Knowledge base data processing tools, on a fresh look at the current mainstream document processing projects, including olmOCR, Marker, MinerU, Docling, Markitdown, Llamaparse the six tools, and a brief comparison of them. To summarize. MinerU Document extraction is more general, suitable for all kinds of scenarios, but the other document proposed tools have their own characteristics, please choose according to their own needs.

olmOCR

Technical Architecture: Based on the large language model to build a complete PDF processing process. It uses a distributed architecture, supports single and multi-node parallel processing, and uses sglang to achieve GPU-accelerated reasoning.

Functional features: with high-quality text extraction capabilities to extract structured plain text from complex PDFs, correctly handling multi-column layouts, tables, mathematical equations and handwritten content. Outputs results in Markdown format; costs about $190 to process 1,000,000 PDF pages; also outperforms Marker, MinerU and GOT-OCR 2.0 and other similar tools.

Applicable scenarios: digitization of academic documents, conversion of enterprise-level document repositories, construction of AI training datasets, and historical document content recovery.

✅ Advantage: open source project, high parsing quality, lower cost than commercial APIs, outstanding performance.

❎ shortcomings: the use of a higher threshold, the need for a variety of system dependencies; is still in the early stages of development, the documentation needs to be improved; currently only supports parsing PDF and images.

https://github.com/allenai/olmocr

Marker

Technical architecture: based on PyMuPDF and Tesseract OCR, support for GPU acceleration (Surya OCR engine), open source lightweight.

Features: Focus on PDF to Markdown, support for formula to LaTeX, image inline preservation, OCR recognition of scanned PDF, can handle multi-language documents.

Scenario: for scientific research literature, books and other basic PDF conversion needs, suitable for users with a technical background for rapid deployment.

✅ Advantage: open source and free, fast processing speed (4 times faster than similar).

🙅‍♀️ Shortcomings: lacks complex layout parsing capabilities and relies on local GPU resources.

https://github.com/VikParuchuri/marker

MinerU

Technical Architecture: Integrate LayoutLMv3, YOLOv8 and other models, support multimodal parsing (table/formula/image), rely on Docker and CUDA environment.

Features: Accurate extraction of PDF text, automatic filtering header/footer, support for EPUB/MOBI/DOCX to Markdown or JSON, multi-language OCR (84 languages), built-in UniMERNet model optimized for formula recognition.

Applicable scenarios: suitable for academic literature management, financial statement analysis and other scenarios that require high-precision structuring.

✅ Advantage: enterprise-grade security compliance with API and GUI support.

🙅Deficiencies: reliance on GPUs, slower form processing, complex configuration.

https://github.com/opendatalab/MinerU

Docling

Technical architecture: modular design, integration of Unstructured, LayoutParser and other libraries, support for localization processing.

Functional features: parse PDF/DOCX/PPTX and other formats, retain the reading order and table structure, support OCR and LangChain integration, output Markdown or JSON.

Applicable scenarios: suitable for enterprise contract resolution, report automation and other complex applications that need to be combined with AI framework.

✅ Advantage: Compatible with IBM ecology and supports multi-format mixed processing.

🙅‍♀️ Insufficient: CUDA environment is required, and some functions rely on commercial models.

https://github.com/DS4SD/docling

Markitdown

Technical Architecture: Microsoft open source project , integrated GPT - 4 and other models to achieve AI enhancement processing , support for multi-format conversion .

Features: Support Word/Excel/PPT, image (OCR), audio (voice transcription) to Markdown, batch processing of ZIP files, can generate image descriptions (OpenAI API required).

Scenario: suitable for multi-format mixed content creation, such as PPT charts to documents, audio and video transcription.

✅ Advantage: most complete format support, developer friendly (Python API/CLI).

🙅‍♀️ deficiencies: reliance on external APIs, some features require paid models.

https://github.com/microsoft/markitdown

Llamaparse

Technical architecture: designed for RAG, combining Azure OpenAI and KDB AI vector database to optimize semantic retrieval.

Features: Parses complex PDFs containing tables/charts, outputs Markdown/LaTeX/Mermaid charts, supports generation of knowledge graphs, enterprise-level security compliance.

Applicable scenarios: for legal document analysis, technical manual Q&A and other intelligent applications that need to be combined with LLM.

✅ Advantage: high parsing accuracy, support semantic optimization of semi-structured data.

🙅‍♂️ Shortcomings: slow processing speed, limited free credits, API key required.

https://docs.llamaindex.ai/en/stable/llama_cloud/llama_parse