PaddleOCR vs MinerU vs RAGFlow vs Umi-OCR: 2026 Objective Comparison
Quick Comparison
| Aspect | PaddleOCR | MinerU | RAGFlow | Umi-OCR |
|---|---|---|---|---|
| Core Focus | Lightweight OCR + document parsing toolkit | End-to-end PDF/image/DOCX to Markdown/JSON | RAG engine with integrated document parsing | Desktop GUI batch OCR tool |
| Model Size | PP-OCR series + VL-1.5 (0.9B) | ~1.2B components (v2.5) | Uses PaddleOCR-VL backend | Relies on PaddleOCR backend |
| OmniDocBench v1.5 | 94.5% (PaddleOCR-VL-1.5) | ~90.67% (MinerU 2.5) | Depends on backend (~90–94.5%) | Depends on backend (~94.5% max) |
| Inference Speed | Fastest (reference baseline) | Moderate (14–15% slower than PaddleOCR-VL in tests) | Pipeline overhead | Fast for desktop batch images |
| Languages Supported | 109+ (strong multilingual incl. Tibetan, Bengali) | 109+ (inherits from backend) | Inherits from backend | 80+ via engine |
| Layout & Structure | Excellent tables, formulas, seals, irregular boxes, cross-page | Strong reading order, header/footer removal, complex layouts | Chunking for RAG, visual inspection | Basic image-level, limited structure |
| Deployment | Python API, CLI, CPU/GPU/edge | Python pipeline, Docker | Web UI + server deployment | Windows desktop GUI (offline) |
| License | Apache 2.0 | AGPL-3.0 | Apache 2.0 | Open-source (permissive) |
| GitHub Stars (2026) | ~73k+ | ~57.6k | High (RAG-focused) | Moderate (desktop tool) |
Key Trade-off: PaddleOCR provides the highest raw accuracy and flexibility as a foundational toolkit. MinerU adds polished end-to-end parsing. RAGFlow focuses on full RAG workflows. Umi-OCR prioritizes simple desktop usage.
Performance
PaddleOCR-VL-1.5 (0.9B parameters, January 2026 release) scores 94.5% overall on OmniDocBench v1.5, leading in text-edit distance (0.035), formula recognition (94.21%), table TEDS (92.76%), and real-world distortion scenarios (skew, warping, scanning, screen photos, lighting).
MinerU 2.5 scores ~90.67% on the same benchmark, performing well on complex layouts but trailing in raw OCR metrics and speed. Tests show MinerU 2.5 inference ~14–15% slower than PaddleOCR-VL-1.5.
RAGFlow and Umi-OCR inherit performance from their backend (typically PaddleOCR-VL). RAGFlow adds pipeline overhead for chunking; Umi-OCR matches core OCR accuracy on images but lacks advanced multi-page structure handling.
Real-world scenarios: PaddleOCR excels on multilingual, handwritten, and distorted documents. MinerU better handles semantic coherence in academic PDFs. Umi-OCR suits quick screenshot batches.
Features
- PaddleOCR: Full pipeline including detection, recognition, layout analysis (PP-StructureV3), irregular box positioning, seal recognition, cross-page table merging, and multi-element support (tables, formulas, checkboxes, underlines). Outputs structured Markdown/JSON/HTML.
- MinerU: End-to-end conversion of PDF/image/DOCX with header/footer/footnote removal, reading-order sorting, table-to-HTML, and semantic coherence. Supports scanned/garbled PDFs with automatic OCR fallback.
- RAGFlow: Integrates PaddleOCR-VL via DeepDoc for document ingestion, visual chunking, template-based processing, and RAG-specific preprocessing (citations, agent capabilities).
- Umi-OCR: GUI-focused batch processing, screen capture, ignore regions, watermark handling, and simple Markdown export. Limited to image/PDF OCR without deep layout reconstruction.
Trade-off: PaddleOCR maximizes customization and low-level control. MinerU/RAGFlow trade some flexibility for higher-level abstractions and workflow integration.
Ease of Use
- PaddleOCR: Python API and CLI; one-line inference possible after PaddlePaddle setup. Steeper learning curve for beginners but extensive documentation for custom pipelines.
- MinerU: Simple CLI (
mineru pdf2md) and Python library; one-command conversion with improved DOCX support in later versions. - RAGFlow: Web UI for upload, parsing, and knowledge base management; minimal coding for basic RAG workflows.
- Umi-OCR: Easiest — native Windows desktop GUI with drag-and-drop or screen capture; no framework installation required.
All support local/offline deployment. PaddleOCR offers broadest hardware compatibility (including heterogeneous chips).
Ecosystem and Integrations
PaddleOCR serves as the core OCR engine for MinerU, RAGFlow, and Umi-OCR, allowing seamless upgrades when the backend improves.
MinerU and RAGFlow produce LLM-friendly outputs compatible with LangChain/LlamaIndex. PaddleOCR integrates with Hugging Face, ComfyUI, and custom pipelines. Umi-OCR remains primarily standalone for desktop use.
All are open-source with active communities and no mandatory cloud dependencies.
Pricing and Licensing
All tools are free and self-hosted with no usage fees:
- PaddleOCR: Apache 2.0 (most permissive for derivatives).
- MinerU: AGPL-3.0 (copyleft requirements for modifications/distribution).
- RAGFlow: Apache 2.0.
- Umi-OCR: Open-source permissive license.
No paid tiers; commercial use possible within license terms.
Which Should You Choose?
Choose PaddleOCR for building custom OCR pipelines, edge deployment, or maximum accuracy/flexibility on distorted/multilingual documents. Ideal for developers needing low-level control.
Choose MinerU when requiring polished end-to-end PDF/DOCX-to-Markdown conversion with clean semantic output for RAG preparation or knowledge bases.
Choose RAGFlow for complete RAG systems that include document parsing, chunking, visual inspection, and agent features in one platform.
Choose Umi-OCR for simple, no-code desktop batch OCR on screenshots or scanned images where GUI convenience is priority.
Common hybrid: Use PaddleOCR as backend + MinerU or RAGFlow for higher-level tasks, with Umi-OCR for daily quick scans. Test each tool on your specific document types since all are free to run locally.