0a52cff8a7
- Added extract_meaning_kernels.py for processing PDF diagrams - Extracts text using OCR (Tesseract) when available - Analyzes diagram structure (type, dimensions, orientation) - Generates structured meaning kernels with metadata - Outputs JSON (machine-readable) and Markdown (human-readable) - Includes test pipeline and documentation - Supports single files and batch processing Pipeline components: - DiagramProcessor: Main processing engine - MeaningKernel: Structured kernel representation - PDF to image conversion - OCR text extraction - Structure analysis - Kernel generation with confidence scoring 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
129 lines
3.5 KiB
Markdown
129 lines
3.5 KiB
Markdown
# Multimodal Meaning Kernel Extraction Pipeline
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Extracts structured meaning kernels from academic PDF diagrams into text format.
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## Issue #493
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[Multimodal] Extract Meaning Kernels from Research Diagrams
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## Overview
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This pipeline processes academic PDF diagrams and images to extract structured "meaning kernels" - discrete units of meaning that can be stored, indexed, and analyzed.
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## Features
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- **PDF Processing**: Converts PDF pages to images and processes each page
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- **OCR Text Extraction**: Extracts text from diagrams using Tesseract OCR
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- **Structure Analysis**: Analyzes diagram structure (type, dimensions, orientation)
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- **Kernel Generation**: Creates structured meaning kernels with metadata
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- **Multiple Output Formats**: JSON for machine processing, Markdown for human readability
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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/multimodal/extract_meaning_kernels.py research_paper.pdf
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# Process a single image
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python3 scripts/multimodal/extract_meaning_kernels.py diagram.png
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# Process a directory of files
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python3 scripts/multimodal/extract_meaning_kernels.py /path/to/diagrams/
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# Specify output directory
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python3 scripts/multimodal/extract_meaning_kernels.py paper.pdf -o ./output
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# Use configuration file
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python3 scripts/multimodal/extract_meaning_kernels.py paper.pdf -c config.json
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```
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## Output Structure
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For each processed file, the pipeline creates:
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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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## Meaning Kernel Format
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Each kernel contains:
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```json
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{
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"kernel_id": "kernel_20260413_181234_p1_text",
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"content": "Extracted text content from the diagram",
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"source": "path/to/source/file.png",
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"confidence": 0.85,
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"metadata": {
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"type": "text_extraction",
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"word_count": 42,
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"line_count": 5,
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"structure": {...}
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},
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"timestamp": "2026-04-13T18:12:34.567890",
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"hash": "a1b2c3d4e5f6g7h8"
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}
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```
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## Kernel Types
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1. **Text Extraction**: Direct OCR text from the diagram
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2. **Structure Analysis**: Diagram type, dimensions, orientation
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3. **Summary**: Combined analysis of text and structure
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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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"output_format": ["json", "markdown"],
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"verbose": true
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}
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```
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## Use Cases
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- **Research Analysis**: Extract key concepts from academic papers
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- **Knowledge Graphs**: Build structured knowledge from visual information
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- **Document Indexing**: Make diagram content searchable
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- **Content Summarization**: Generate text summaries of visual content
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- **Machine Learning**: Training data for multimodal AI models
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## Limitations
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- OCR quality depends on diagram clarity and resolution
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- Structure analysis is simplified (real CV would be more accurate)
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- Complex diagrams may need specialized processing
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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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- Specialized processors for different diagram types
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- Integration with LLMs for semantic analysis
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- Batch processing with parallelization
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- API endpoint for web integration
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