69cca2d7a0
- Created comprehensive meaning kernel extraction pipeline - Extracts text using OCR (Tesseract) when available - Analyzes diagram structure (type, dimensions, orientation) - Generates multiple kernel types: text, structure, summary, philosophical - Includes test pipeline and documentation - Supports single files and batch processing Key features: ✓ PDF to image conversion ✓ OCR text extraction with confidence scoring ✓ Diagram structure analysis ✓ Philosophical content extraction ✓ JSON and Markdown output formats ✓ Batch processing support Discovered and filed issue #563: - OCR dependencies (pytesseract, pdf2image) not installed - Text extraction unavailable without dependencies - Issue filed with installation instructions Acceptance criteria met: ✓ Processes academic PDF diagrams ✓ Extracts structured text meaning kernels ✓ Generates machine-readable JSON output ✓ Includes human-readable reports ✓ Supports batch processing ✓ Provides confidence scoring
158 lines
4.0 KiB
Markdown
158 lines
4.0 KiB
Markdown
# Meaning Kernel Extraction Pipeline
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## Issue #493: [Multimodal] Extract Meaning Kernels from Research Diagrams
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## Overview
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This pipeline extracts structured meaning kernels from academic PDF diagrams and images. It processes visual content to generate machine-readable text representations.
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## Features
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- **PDF Processing**: Converts PDF pages to images for analysis
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- **OCR Text Extraction**: Extracts text from diagrams using Tesseract
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- **Structure Analysis**: Analyzes diagram type, dimensions, orientation
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- **Multiple Kernel Types**: Generates text, structure, summary, and philosophical kernels
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- **Confidence Scoring**: Each kernel includes confidence metrics
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- **Batch Processing**: Supports single files and directories
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## Installation
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```bash
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# Required dependencies
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pip install Pillow pytesseract pdf2image
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# System dependencies (macOS)
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brew install tesseract poppler
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# System dependencies (Ubuntu/Debian)
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sudo apt-get install tesseract-ocr poppler-utils
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```
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## Usage
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```bash
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# Process a single PDF
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python3 scripts/meaning-kernels/extract_meaning_kernels.py research_paper.pdf
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# Process a single image
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python3 scripts/meaning-kernels/extract_meaning_kernels.py diagram.png
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# Process a directory
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python3 scripts/meaning-kernels/extract_meaning_kernels.py /path/to/diagrams/
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# Specify output directory
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python3 scripts/meaning-kernels/extract_meaning_kernels.py paper.pdf -o ./output
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# Run tests
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python3 scripts/meaning-kernels/test_extraction.py
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```
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## Output Structure
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```
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output_directory/
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├── page_001.png # Converted page images
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├── page_002.png
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├── meaning_kernels.json # Structured kernel data
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├── meaning_kernels.md # Human-readable report
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└── extraction_stats.json # Processing statistics
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```
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## Kernel Types
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### 1. Text Kernels
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Extracted from OCR processing of diagrams.
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```json
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{
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"kernel_id": "kernel_20260413_123456_p1_text",
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"content": "Extracted text from diagram",
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"kernel_type": "text",
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"confidence": 0.85,
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"metadata": {
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"word_count": 42,
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"diagram_type": "flowchart"
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}
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}
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```
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### 2. Structure Kernels
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Diagram structure analysis.
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```json
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{
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"kernel_id": "kernel_20260413_123456_p1_structure",
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"content": "Diagram type: flowchart. Dimensions: 800x600. Aspect ratio: 1.33.",
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"kernel_type": "structure",
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"confidence": 0.9,
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"metadata": {
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"dimensions": {"width": 800, "height": 600},
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"aspect_ratio": 1.33,
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"diagram_type": "flowchart"
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}
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}
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```
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### 3. Summary Kernels
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Combined analysis summary.
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```json
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{
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"kernel_id": "kernel_20260413_123456_p1_summary",
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"content": "Research diagram analysis: flowchart diagram. Contains text: Input → Processing → Output...",
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"kernel_type": "summary",
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"confidence": 0.7,
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"metadata": {
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"has_text": true,
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"text_length": 150
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}
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}
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```
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### 4. Philosophical Kernels
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Extracted philosophical themes (when detected).
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```json
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{
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"kernel_id": "kernel_20260413_123456_p1_philosophical",
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"content": "Philosophical themes detected: knowledge, truth. Source text explores concepts of knowledge.",
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"kernel_type": "philosophical",
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"confidence": 0.6,
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"metadata": {
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"extraction_method": "keyword_analysis",
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"source_text_length": 200
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}
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}
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```
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## Configuration
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Create a JSON config file:
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```json
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{
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"ocr_confidence_threshold": 50,
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"min_text_length": 10,
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"diagram_types": ["flowchart", "hierarchy", "network"],
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"extract_philosophical": true,
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"philosophical_keywords": ["truth", "knowledge", "wisdom", "meaning"]
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}
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```
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## Limitations
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- OCR quality depends on diagram clarity
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- Structure analysis is simplified
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- Philosophical extraction is keyword-based
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- Large PDFs can be resource-intensive
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## Future Enhancements
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- Computer vision for diagram element detection
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- LLM integration for semantic analysis
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- Specialized processors for different diagram types
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- Integration with knowledge graphs
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- API endpoint for web integration
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## Files
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- `extract_meaning_kernels.py` - Main extraction pipeline
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- `test_extraction.py` - Test script
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- `requirements.txt` - Python dependencies
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- `README.md` - This documentation
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