3.9 KiB
3.9 KiB
PDF Tool Comparison
Summary Table
| Tool | Type | Speed | Quality Issues | Garbled | Structure | License |
|---|---|---|---|---|---|---|
| Docling | ML | 0.43s/page | 50 | 0 | ✓ Yes | Apache 2.0 |
| PyMuPDF | Traditional | 0.01s/page | 1 | 0 | ✗ No | AGPL |
| pdfplumber | Traditional | 0.44s/page | 0 | 0 | ✗ No | MIT |
| pdftotext | Traditional | 0.007s/page | 90 | 0 | ✗ No | GPL |
| pdfminer.six | Traditional | 0.15s/page | 45 | 0 | ✗ No | MIT |
| pypdf | Traditional | 0.25s/page | 120 | 5 | ✗ No | BSD |
Test environment: 90-page academic PDF, 1.9 MB
Detailed Comparison
Docling (Recommended for Academic PDFs)
Advantages:
- Only tool that preserves structure (headers, tables, lists)
- AI-powered layout understanding via RT-DETR + TableFormer
- Markdown output perfect for LLMs
- 97.9% table accuracy in enterprise benchmarks
- On-device processing (no API calls)
Disadvantages:
- Slower than PyMuPDF (40x)
- Requires 500MB-1GB model download
- Some ligature encoding issues
Use when:
- Document structure is essential
- Processing academic papers with tables
- Preparing content for RAG systems
- LLM consumption is primary goal
PyMuPDF (Recommended for Speed)
Advantages:
- Fastest tool (60x faster than pdfplumber)
- Excellent quality (only 1 issue in test)
- Clean output with minimal artifacts
- C-based, highly optimized
Disadvantages:
- No structure preservation
- AGPL license (restrictive for commercial use)
- Flat text output
Use when:
- Speed is critical
- Simple text extraction sufficient
- Batch processing large datasets
- Structure preservation not needed
pdfplumber (Recommended for Quality)
Advantages:
- Perfect quality (0 issues)
- Character-level spatial analysis
- Geometric table detection
- MIT license
Disadvantages:
- Very slow (60x slower than PyMuPDF)
- No markdown structure output
- CPU-intensive
Use when:
- Maximum fidelity required
- Quality more important than speed
- Processing critical documents
- Slow processing acceptable
Traditional vs ML-Based
Traditional Tools
How they work:
- Parse PDF internal structure
- Extract embedded text objects
- Follow PDF specification rules
Advantages:
- Fast (no ML inference)
- Small footprint (no model files)
- Deterministic output
Disadvantages:
- No layout understanding
- Cannot handle borderless tables
- Lose document hierarchy
ML-Based Tools (Docling)
How they work:
- Computer vision to "see" document layout
- RT-DETR detects layout regions
- TableFormer understands table structure
- Hybrid: ML for layout + PDF parsing for text
Advantages:
- Understands visual layout
- Handles complex multi-column layouts
- Preserves semantic structure
- Works with borderless tables
Disadvantages:
- Slower (ML inference time)
- Larger footprint (model files)
- Non-deterministic output
Architecture Details
Docling Pipeline
- PDF Backend - Extracts raw content and positions
- AI Models - Analyze layout and structure
- RT-DETR: Layout analysis (44-633ms/page)
- TableFormer: Table structure (400ms-1.74s/table)
- Assembly - Combines understanding with text
pdfplumber Architecture
- Built on pdfminer.six - Character-level extraction
- Spatial clustering - Groups chars into words/lines
- Geometric detection - Finds tables from lines/rectangles
- Character objects - Full metadata (position, font, size, color)
Enterprise Benchmarks (2025 Procycons)
| Tool | Table Accuracy | Text Fidelity | Speed (s/page) |
|---|---|---|---|
| Docling | 97.9% | 100% | 6.28 |
| Marker | 89.2% | 98.5% | 8.45 |
| MinerU | 92.1% | 99.2% | 12.33 |
| Unstructured.io | 75.0% | 95.8% | 51.02 |
| LlamaParse | 88.5% | 97.3% | 6.00 |
Source: Procycons Enterprise PDF Processing Benchmark 2025