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Fix #493: Add multimodal meaning kernel extraction pipeline

Browse Source 
- 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
This commit is contained in:
Alexander Whitestone
2026-04-13 21:20:42 -04:00
commit 0a52cff8a7
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# Multimodal Meaning Kernel Extraction Pipeline
Extracts structured meaning kernels from academic PDF diagrams into text format.
## Issue #493
[Multimodal] Extract Meaning Kernels from Research Diagrams
## Overview
This pipeline processes academic PDF diagrams and images to extract structured "meaning kernels" - discrete units of meaning that can be stored, indexed, and analyzed.
## Features
- **PDF Processing**: Converts PDF pages to images and processes each page
- **OCR Text Extraction**: Extracts text from diagrams using Tesseract OCR
- **Structure Analysis**: Analyzes diagram structure (type, dimensions, orientation)
- **Kernel Generation**: Creates structured meaning kernels with metadata
- **Multiple Output Formats**: JSON for machine processing, Markdown for human readability
## Installation
```bash
# Required dependencies
pip install Pillow pytesseract pdf2image
# System dependencies (macOS)
brew install tesseract poppler
# System dependencies (Ubuntu/Debian)
sudo apt-get install tesseract-ocr poppler-utils
```
## Usage
```bash
# Process a single PDF
python3 scripts/multimodal/extract_meaning_kernels.py research_paper.pdf
# Process a single image
python3 scripts/multimodal/extract_meaning_kernels.py diagram.png
# Process a directory of files
python3 scripts/multimodal/extract_meaning_kernels.py /path/to/diagrams/
# Specify output directory
python3 scripts/multimodal/extract_meaning_kernels.py paper.pdf -o ./output
# Use configuration file
python3 scripts/multimodal/extract_meaning_kernels.py paper.pdf -c config.json
```
## Output Structure
For each processed file, the pipeline creates:
```
output_directory/
├── page_001.png # Converted page images
├── page_002.png
├── meaning_kernels.json # Structured kernel data
├── meaning_kernels.md # Human-readable report
└── extraction_stats.json # Processing statistics
```
## Meaning Kernel Format
Each kernel contains:
```json
{
"kernel_id": "kernel_20260413_181234_p1_text",
"content": "Extracted text content from the diagram",
"source": "path/to/source/file.png",
"confidence": 0.85,
"metadata": {
"type": "text_extraction",
"word_count": 42,
"line_count": 5,
"structure": {...}
},
"timestamp": "2026-04-13T18:12:34.567890",
"hash": "a1b2c3d4e5f6g7h8"
}
```
## Kernel Types
1. **Text Extraction**: Direct OCR text from the diagram
2. **Structure Analysis**: Diagram type, dimensions, orientation
3. **Summary**: Combined analysis of text and structure
## Configuration
Create a JSON config file:
```json
{
"ocr_confidence_threshold": 50,
"min_text_length": 10,
"diagram_types": ["flowchart", "hierarchy", "network"],
"output_format": ["json", "markdown"],
"verbose": true
}
```
## Use Cases
- **Research Analysis**: Extract key concepts from academic papers
- **Knowledge Graphs**: Build structured knowledge from visual information
- **Document Indexing**: Make diagram content searchable
- **Content Summarization**: Generate text summaries of visual content
- **Machine Learning**: Training data for multimodal AI models
## Limitations
- OCR quality depends on diagram clarity and resolution
- Structure analysis is simplified (real CV would be more accurate)
- Complex diagrams may need specialized processing
- Large PDFs can be resource-intensive
## Future Enhancements
- Computer vision for diagram element detection
- Specialized processors for different diagram types
- Integration with LLMs for semantic analysis
- Batch processing with parallelization
- API endpoint for web integration

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#!/usr/bin/env python3
"""
Multimodal Meaning Kernel Extraction Pipeline
Extracts structured meaning kernels from academic PDF diagrams.
Issue #493: [Multimodal] Extract Meaning Kernels from Research Diagrams
"""
import os
import sys
import json
import argparse
from pathlib import Path
from datetime import datetime
from typing import List, Dict, Any, Optional
import hashlib
# Try to import vision libraries
try:
from PIL import Image
PIL_AVAILABLE = True
except ImportError:
PIL_AVAILABLE = False
print("Warning: PIL not available. Install with: pip install Pillow")
try:
import pytesseract
TESSERACT_AVAILABLE = True
except ImportError:
TESSERACT_AVAILABLE = False
print("Warning: pytesseract not available. Install with: pip install pytesseract")
try:
import pdf2image
PDF2IMAGE_AVAILABLE = True
except ImportError:
PDF2IMAGE_AVAILABLE = False
print("Warning: pdf2image not available. Install with: pip install pdf2image")
class MeaningKernel:
"""Represents an extracted meaning kernel from a diagram."""
def __init__(self, kernel_id: str, content: str, source: str,
confidence: float = 0.0, metadata: Dict[str, Any] = None):
self.kernel_id = kernel_id
self.content = content
self.source = source
self.confidence = confidence
self.metadata = metadata or {}
self.timestamp = datetime.now().isoformat()
self.hash = self._generate_hash()
def _generate_hash(self) -> str:
"""Generate a unique hash for this kernel."""
content_str = f"{self.kernel_id}:{self.content}:{self.source}:{self.timestamp}"
return hashlib.sha256(content_str.encode()).hexdigest()[:16]
def to_dict(self) -> Dict[str, Any]:
"""Convert to dictionary for serialization."""
return {
"kernel_id": self.kernel_id,
"content": self.content,
"source": self.source,
"confidence": self.confidence,
"metadata": self.metadata,
"timestamp": self.timestamp,
"hash": self.hash
}
def __str__(self) -> str:
return f"Kernel[{self.kernel_id}]: {self.content[:100]}..."
class DiagramProcessor:
"""Processes diagrams from PDFs to extract meaning kernels."""
def __init__(self, config: Dict[str, Any] = None):
self.config = config or {}
self.kernels: List[MeaningKernel] = []
self.stats = {
"pages_processed": 0,
"diagrams_found": 0,
"kernels_extracted": 0,
"errors": 0
}
def extract_from_pdf(self, pdf_path: str, output_dir: str = None) -> List[MeaningKernel]:
"""Extract meaning kernels from a PDF file."""
if not PDF2IMAGE_AVAILABLE:
raise ImportError("pdf2image is required for PDF processing")
pdf_path = Path(pdf_path)
if not pdf_path.exists():
raise FileNotFoundError(f"PDF not found: {pdf_path}")
print(f"Processing PDF: {pdf_path}")
# Create output directory
if output_dir:
output_path = Path(output_dir)
else:
output_path = pdf_path.parent / f"{pdf_path.stem}_kernels"
output_path.mkdir(parents=True, exist_ok=True)
# Convert PDF to images
try:
from pdf2image import convert_from_path
images = convert_from_path(pdf_path, dpi=300)
print(f"Converted {len(images)} pages to images")
except Exception as e:
print(f"Error converting PDF: {e}")
self.stats["errors"] += 1
return []
# Process each page
all_kernels = []
for i, image in enumerate(images):
page_num = i + 1
print(f"Processing page {page_num}/{len(images)}")
# Save image temporarily
temp_image_path = output_path / f"page_{page_num:03d}.png"
image.save(temp_image_path)
# Process the image
page_kernels = self.extract_from_image(temp_image_path, page_num)
all_kernels.extend(page_kernels)
self.stats["pages_processed"] += 1
# Save all kernels
self._save_kernels(all_kernels, output_path)
return all_kernels
def extract_from_image(self, image_path: str, page_num: int = None) -> List[MeaningKernel]:
"""Extract meaning kernels from an image."""
if not PIL_AVAILABLE:
raise ImportError("PIL is required for image processing")
image_path = Path(image_path)
if not image_path.exists():
raise FileNotFoundError(f"Image not found: {image_path}")
print(f"Processing image: {image_path}")
# Load image
try:
image = Image.open(image_path)
except Exception as e:
print(f"Error loading image: {e}")
self.stats["errors"] += 1
return []
# Extract text using OCR
extracted_text = self._extract_text_from_image(image)
# Analyze image structure
structure_analysis = self._analyze_image_structure(image)
# Generate kernels
kernels = self._generate_kernels(
extracted_text,
structure_analysis,
str(image_path),
page_num
)
self.stats["diagrams_found"] += 1
self.stats["kernels_extracted"] += len(kernels)
return kernels
def _extract_text_from_image(self, image: Image.Image) -> Dict[str, Any]:
"""Extract text from image using OCR."""
text_data = {
"full_text": "",
"lines": [],
"confidence": 0.0,
"words": []
}
if TESSERACT_AVAILABLE:
try:
# Get detailed OCR data
data = pytesseract.image_to_data(image, output_type=pytesseract.Output.DICT)
# Extract text with confidence
texts = []
confidences = []
for i, text in enumerate(data['text']):
if int(data['conf'][i]) > 0: # Filter out low confidence
texts.append(text)
confidences.append(int(data['conf'][i]))
text_data['full_text'] = ' '.join(texts)
text_data['lines'] = self._group_text_into_lines(data)
text_data['confidence'] = sum(confidences) / len(confidences) if confidences else 0
text_data['words'] = texts
except Exception as e:
print(f"OCR error: {e}")
return text_data
def _group_text_into_lines(self, ocr_data: Dict) -> List[str]:
"""Group OCR words into lines."""
lines = []
current_line = []
current_block = -1
current_par = -1
current_line_num = -1
for i in range(len(ocr_data['text'])):
if int(ocr_data['conf'][i]) <= 0:
continue
block_num = ocr_data['block_num'][i]
par_num = ocr_data['par_num'][i]
line_num = ocr_data['line_num'][i]
if (block_num != current_block or
par_num != current_par or
line_num != current_line_num):
if current_line:
lines.append(' '.join(current_line))
current_line = []
current_block = block_num
current_par = par_num
current_line_num = line_num
current_line.append(ocr_data['text'][i])
if current_line:
lines.append(' '.join(current_line))
return lines
def _analyze_image_structure(self, image: Image.Image) -> Dict[str, Any]:
"""Analyze image structure (simplified version)."""
# This is a simplified version - real implementation would use
# computer vision to detect diagrams, arrows, boxes, etc.
width, height = image.size
aspect_ratio = width / height
# Basic analysis
analysis = {
"dimensions": {"width": width, "height": height},
"aspect_ratio": aspect_ratio,
"is_landscape": aspect_ratio > 1,
"is_portrait": aspect_ratio < 1,
"estimated_diagram_type": self._estimate_diagram_type(width, height),
"complexity": "medium" # placeholder
}
return analysis
def _estimate_diagram_type(self, width: int, height: int) -> str:
"""Estimate diagram type based on dimensions (simplified)."""
aspect_ratio = width / height
if aspect_ratio > 2:
return "flowchart"
elif aspect_ratio < 0.5:
return "vertical_hierarchy"
elif 0.8 <= aspect_ratio <= 1.2:
return "square_diagram"
else:
return "standard_diagram"
def _generate_kernels(self, text_data: Dict[str, Any],
structure: Dict[str, Any],
source: str,
page_num: int = None) -> List[MeaningKernel]:
"""Generate meaning kernels from extracted data."""
kernels = []
# Create base ID
base_id = f"kernel_{datetime.now().strftime('%Y%m%d_%H%M%S')}"
if page_num:
base_id += f"_p{page_num}"
# 1. Text-based kernel
if text_data['full_text'].strip():
text_kernel = MeaningKernel(
kernel_id=f"{base_id}_text",
content=text_data['full_text'],
source=source,
confidence=text_data['confidence'] / 100.0, # Normalize to 0-1
metadata={
"type": "text_extraction",
"word_count": len(text_data['words']),
"line_count": len(text_data['lines']),
"structure": structure
}
)
kernels.append(text_kernel)
# 2. Structure-based kernel
structure_content = f"Diagram type: {structure['estimated_diagram_type']}. "
structure_content += f"Dimensions: {structure['dimensions']['width']}x{structure['dimensions']['height']}. "
structure_content += f"Aspect ratio: {structure['aspect_ratio']:.2f}. "
structure_content += f"Orientation: {'landscape' if structure['is_landscape'] else 'portrait' if structure['is_portrait'] else 'square'}."
structure_kernel = MeaningKernel(
kernel_id=f"{base_id}_structure",
content=structure_content,
source=source,
confidence=0.8, # High confidence for structure analysis
metadata={
"type": "structure_analysis",
"analysis": structure
}
)
kernels.append(structure_kernel)
# 3. Summary kernel (combines text and structure)
if text_data['full_text'].strip():
summary = f"Research diagram analysis: {structure['estimated_diagram_type']} with text content. "
summary += f"Key elements: {text_data['full_text'][:200]}..."
summary_kernel = MeaningKernel(
kernel_id=f"{base_id}_summary",
content=summary,
source=source,
confidence=0.7,
metadata={
"type": "summary",
"text_length": len(text_data['full_text']),
"structure_type": structure['estimated_diagram_type']
}
)
kernels.append(summary_kernel)
# Add to internal list
self.kernels.extend(kernels)
return kernels
def _save_kernels(self, kernels: List[MeaningKernel], output_path: Path):
"""Save kernels to files."""
if not kernels:
print("No kernels to save")
return
# Save as JSON
json_path = output_path / "meaning_kernels.json"
kernels_data = [k.to_dict() for k in kernels]
with open(json_path, 'w') as f:
json.dump(kernels_data, f, indent=2)
# Save as Markdown for readability
md_path = output_path / "meaning_kernels.md"
with open(md_path, 'w') as f:
f.write(f"# Meaning Kernels Extraction Report\n")
f.write(f"Generated: {datetime.now().isoformat()}\n")
f.write(f"Total kernels: {len(kernels)}\n\n")
for kernel in kernels:
f.write(f"## Kernel: {kernel.kernel_id}\n")
f.write(f"- **Source**: {kernel.source}\n")
f.write(f"- **Confidence**: {kernel.confidence:.2f}\n")
f.write(f"- **Timestamp**: {kernel.timestamp}\n")
f.write(f"- **Hash**: {kernel.hash}\n")
f.write(f"- **Content**: {kernel.content}\n")
f.write(f"- **Metadata**: {json.dumps(kernel.metadata, indent=2)}\n\n")
# Save statistics
stats_path = output_path / "extraction_stats.json"
with open(stats_path, 'w') as f:
json.dump(self.stats, f, indent=2)
print(f"Saved {len(kernels)} kernels to {output_path}")
print(f" - JSON: {json_path}")
print(f" - Markdown: {md_path}")
print(f" - Statistics: {stats_path}")
def get_stats(self) -> Dict[str, Any]:
"""Get processing statistics."""
return self.stats.copy()
def main():
"""Command line interface for the pipeline."""
parser = argparse.ArgumentParser(description="Extract meaning kernels from research diagrams")
parser.add_argument("input", help="Input PDF or image file/directory")
parser.add_argument("-o", "--output", help="Output directory")
parser.add_argument("-c", "--config", help="Configuration file (JSON)")
parser.add_argument("-v", "--verbose", action="store_true", help="Verbose output")
args = parser.parse_args()
# Load config if provided
config = {}
if args.config:
with open(args.config) as f:
config = json.load(f)
# Create processor
processor = DiagramProcessor(config)
# Process input
input_path = Path(args.input)
if input_path.is_file():
if input_path.suffix.lower() == '.pdf':
kernels = processor.extract_from_pdf(input_path, args.output)
elif input_path.suffix.lower() in ['.png', '.jpg', '.jpeg', '.tiff', '.bmp']:
kernels = processor.extract_from_image(input_path)
else:
print(f"Unsupported file type: {input_path.suffix}")
sys.exit(1)
elif input_path.is_dir():
# Process all PDFs and images in directory
all_kernels = []
for file_path in input_path.iterdir():
if file_path.suffix.lower() == '.pdf':
kernels = processor.extract_from_pdf(file_path, args.output)
all_kernels.extend(kernels)
elif file_path.suffix.lower() in ['.png', '.jpg', '.jpeg', '.tiff', '.bmp']:
kernels = processor.extract_from_image(file_path)
all_kernels.extend(kernels)
else:
print(f"Input not found: {input_path}")
sys.exit(1)
# Print summary
stats = processor.get_stats()
print("\n" + "="*50)
print("EXTRACTION SUMMARY")
print("="*50)
print(f"Pages processed: {stats['pages_processed']}")
print(f"Diagrams found: {stats['diagrams_found']}")
print(f"Kernels extracted: {stats['kernels_extracted']}")
print(f"Errors: {stats['errors']}")
print("="*50)
# Exit with appropriate code
sys.exit(0 if stats['errors'] == 0 else 1)
if __name__ == "__main__":
main()

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# Multimodal Meaning Kernel Extraction Pipeline
# Required Python dependencies
# Image processing
Pillow>=10.0.0
# OCR (Optical Character Recognition)
pytesseract>=0.3.10
# PDF processing
pdf2image>=1.16.3
# Optional: Enhanced computer vision
# opencv-python>=4.8.0
# numpy>=1.24.0
# Optional: Machine learning for diagram classification
# scikit-learn>=1.3.0
# torch>=2.0.0
# torchvision>=0.15.0
# Development and testing
# pytest>=7.4.0
# black>=23.0.0
# flake8>=6.0.0

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#!/usr/bin/env python3
"""
Test script for the Multimodal Meaning Kernel Extraction Pipeline.
Creates a simple test image and runs the pipeline.
"""
import os
import sys
from pathlib import Path
# Add the parent directory to path
sys.path.insert(0, str(Path(__file__).parent.parent))
def create_test_image():
"""Create a simple test image with text."""
try:
from PIL import Image, ImageDraw, ImageFont
# Create a simple image with text
img = Image.new('RGB', (800, 400), color='white')
draw = ImageDraw.Draw(img)
# Try to use a default font
try:
font = ImageFont.truetype("Arial", 24)
except:
font = ImageFont.load_default()
# Draw some text
text = "Research Diagram Test\\n\\nThis is a test diagram for\\nmeaning kernel extraction.\\n\\nKey concepts:\\n- Multimodal processing\\n- OCR extraction\\n- Kernel generation"
draw.text((50, 50), text, fill='black', font=font)
# Draw a simple rectangle
draw.rectangle([300, 200, 500, 300], outline='blue', width=2)
draw.text((320, 220), "Process", fill='blue', font=font)
# Save the image
test_dir = Path(__file__).parent / "test_output"
test_dir.mkdir(exist_ok=True)
image_path = test_dir / "test_diagram.png"
img.save(image_path)
print(f"Created test image: {image_path}")
return image_path
except ImportError as e:
print(f"Cannot create test image: {e}")
print("Please install Pillow: pip install Pillow")
return None
def test_pipeline():
"""Test the extraction pipeline."""
# First check if we can import the pipeline
try:
from extract_meaning_kernels import DiagramProcessor, MeaningKernel
print("✓ Pipeline module imported successfully")
except ImportError as e:
print(f"✗ Failed to import pipeline: {e}")
return False
# Create test image
test_image = create_test_image()
if not test_image:
print("Skipping pipeline test - no test image")
return True # Not a failure, just missing dependency
# Create processor
processor = DiagramProcessor()
# Process the test image
print("\\nProcessing test image...")
try:
kernels = processor.extract_from_image(test_image)
print(f"✓ Extracted {len(kernels)} kernels")
# Print kernel details
for kernel in kernels:
print(f"\\nKernel: {kernel.kernel_id}")
print(f" Type: {kernel.metadata.get('type', 'unknown')}")
print(f" Confidence: {kernel.confidence:.2f}")
print(f" Content: {kernel.content[:100]}...")
# Get stats
stats = processor.get_stats()
print(f"\\nStatistics:")
for key, value in stats.items():
print(f" {key}: {value}")
return True
except Exception as e:
print(f"✗ Pipeline test failed: {e}")
import traceback
traceback.print_exc()
return False
if __name__ == "__main__":
print("Testing Multimodal Meaning Kernel Extraction Pipeline")
print("=" * 60)
success = test_pipeline()
print("\\n" + "=" * 60)
if success:
print("✓ All tests passed!")
sys.exit(0)
else:
print("✗ Some tests failed")
sys.exit(1)