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skills/pdftext/references/tool-comparison.md

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+# 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
+
+1. **PDF Backend** - Extracts raw content and positions
+2. **AI Models** - Analyze layout and structure
+   - RT-DETR: Layout analysis (44-633ms/page)
+   - TableFormer: Table structure (400ms-1.74s/table)
+3. **Assembly** - Combines understanding with text
+
+### pdfplumber Architecture
+
+1. **Built on pdfminer.six** - Character-level extraction
+2. **Spatial clustering** - Groups chars into words/lines
+3. **Geometric detection** - Finds tables from lines/rectangles
+4. **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*