Which PDF Parser Should You Use? Comparing Docling, Marker, MinerU, olmOCR - and Why NetMind ParsePro Might Be Better

Parsing information from PDFs isn’t just about extracting text anymore. For modern AI workflows, especially Retrieval-Augmented Generation (RAG), you need tools that can reliably understand complex layouts, tables, formulas, and even scanned images. Whether you're building a chatbot, automating report processing, or structuring financial documents, the quality of your parser matters. We reviewed four leading open-source tools, Docling, Marker, MinerU, and olmOCR, and also looked at a commercial alternative, NetMind ParsePro. Here's what you should know.

Docling (IBM)

Best for: Enterprise AI workflows and knowledge base construction

Input formats: PDF, DOCX, PPTX, HTML, Images

Output formats: Markdown, HTML, JSON

Architecture: Layout analysis (DocLayNet) + table recognition (TableFormer)

Model type: Modular NLP and layout models (not vision-language)

OCR support: Basic OCR via layout models (language coverage not specified)

Key features:

• Extracts reading order, tables, code blocks, and mathematical formulas
• Integrates easily with AI pipelines (e.g., LlamaIndex, LangChain)
• Python API and CLI support

Marker (DataLab)

Best for: Fast and flexible PDF conversion across formats

Input formats: PDF, Images, PPTX, DOCX, XLSX, HTML, EPUB

Output formats: Markdown, HTML, JSON

Architecture: Integrated layout analysis and OCR pipeline

Model type: Lightweight visual parsing with acceleration options (CPU/GPU/MPS)

OCR support: 90+ languages

Key features:

• Extracts tables, formulas, images, citations, and code blocks
• Fast runtime with multi-device acceleration
• LLM-enhancement interface for post-processing

MinerU (OpenDataLab)

Best for: Chinese-language, scientific, and financial documents

Input formats: PDF

Output formats: Markdown, JSON

Architecture: PDF-Extract-Kit with hybrid rule-based and pretrained models

Model type: NLP + layout parser with heuristics

OCR support: 84 languages

Key features:

• High accuracy for rotated and large tables
• Specialized in header/footer removal
• Strong structural preservation for multilingual documents

olmOCR (AllenAI)

Best for: Complex multi-column or archival documents

Input formats: PDF, PNG, JPEG

Output formats: Plain text, Markdown

Architecture: Vision-Language Model with visual anchoring

Model type: ~7B parameter VLM

OCR support: Embedded in VLM (language coverage not specified)

Key features:

• Handles complex layouts and visual-text fusion
• Optimized for batch processing
• GPU required; cost estimated at ~$190 per million pages

Performance Comparison

TED-Struct Scores (Higher = Better Structural Preservation)

English documents: Mixed results, recommend testing with your own data

Chinese documents: MinerU scored a perfect 1.000

Japanese documents: MinerU outperformed Marker

Tool Selection by Scenario

Hybrid Workflow Suggestion

For best results in demanding environments:

• Use Marker for initial parsing
• Refine structure with MinerU or olmOCR
• Integrate into workflows using Docling (Note: This adds complexity, but increases overall accuracy.)

A Simpler, Cost-Efficient Alternative: NetMind ParsePro

Open-source solutions offer flexibility, but they come with challenges:

• Difficult setup and configuration
• Constant GPU requirements
• 10–15% lower accuracy compared to commercial APIs

NetMind ParsePro addresses all three. Here are its key advantages:

• Free tier available: 500 pages/month
• Runs on high-performance H100/A100 GPU clusters with enterprise-grade reliability
• Secure by design: Encrypted, sharded data processing
• Asynchronous engine optimized for batch workflows

As for a real world example, Orbit, a financial AI company migrated from Azure’s PDF API to NetMind ParsePro. Here were their results:

• Their monthly cost dropped from $12,000 to $1,200
• Table parsing accuracy increased from 85% to 87%
• Try out our NetMind ParsePro here!

Overall, if you're parsing PDFs for AI or document workflows, tool selection depends on your priorities:

• Use open-source if you want control and can handle the overhead.
• Use NetMind ParsePro if you need speed, accuracy, and low cost with minimal setup.
• The future of document parsing won’t be decided by open vs. closed source. It’ll be decided by who can deliver reliable, frictionless results at scale.