| name | pdf-page-extract |
| description | Extract rich data from PDF pages including text spans with metadata, rendered PNG images, and page mapping. Creates persistent artifacts for downstream processing. |
PDF Page Extract Skill
Purpose
This skill extracts all necessary data from PDF pages to enable accurate AI-driven HTML generation. It produces three critical artifacts:
- Rich extraction data - Text spans with font metadata (sizes, styles, positions)
- Rendered PNG image - Visual reference for AI to understand page layout
- Page mapping - Authoritative mapping of PDF indices to book pages
This is the deterministic, Python-based foundation for the entire pipeline. All extracted data is saved to persistent files for traceability and future processing.
What to Do
Validate input parameters
- Check PDF file exists and is readable
- Verify page range (PDF indices or book pages)
- Confirm output directory structure
Establish page mapping (if not already done)
- Run:
python3 Calypso/tools/read_page_footers.py - Scans page footers to establish PDF index → book page mapping
- Saves to:
analysis/page_mapping.json
- Run:
Extract rich page data using PyMuPDF and pdfplumber
- Run:
python3 Calypso/tools/rich_extractor.py - Extracts text spans with font metadata:
- Font name and size
- Bold/italic flags
- Position (bounding box)
- Color information
- Analyzes page structure to identify:
- Likely headings (by size and style)
- Paragraphs (regular text)
- Potential lists
- Detects tables using pdfplumber
- Saves to:
analysis/chapter_XX/rich_extraction.json
- Run:
Render PDF page to PNG
- Convert page to high-resolution PNG image (300+ DPI)
- Maintains visual fidelity for AI reference
- Saves to:
output/chapter_XX/page_artifacts/page_YY/02_page_XX.png
Extract embedded images (if present)
- Run:
python3 Calypso/tools/extract_images.py - Extracts all images from page
- Saves:
output/chapter_XX/images/page_YY_image_*.png - Creates metadata:
page_YY_images.json
- Run:
Validate extraction completeness
- Verify all files saved correctly
- Check JSON files are valid
- Confirm PNG image is readable
- Validate page mapping consistency
Input Parameters
chapter: <int> - Chapter number (1-8)
start_page: <int> - Starting PDF index (0-based) or page range
end_page: <int> - Ending PDF index (optional if single page)
pdf_path: <str> - Path to PDF file (default: Calypso/PREP-AL 4th Ed 9-26-25.pdf)
output_base: <str> - Output directory (default: Calypso/output)
mapping_file: <str> - Page mapping file (default: Calypso/analysis/page_mapping.json)
Output Structure
Artifact Files Saved
Per-page artifacts (in output/chapter_XX/page_artifacts/page_YY/):
01_rich_extraction.json- Text spans with metadata02_page_XX.png- Rendered PDF page imagepage_mapping.json- Shared mapping file (symlink or copy)
Extraction data (in analysis/chapter_XX/):
rich_extraction.json- Full extraction for all pages in chapterpage_6_pattern_analysis.json- (Optional) Pattern analysis for specific pages
Images (in output/chapter_XX/images/chapter_XX/):
page_XX_image_*.png- Embedded images from pagepage_XX_images.json- Metadata for embedded images
Rich Extraction JSON Format
{
"page_number": 16,
"pdf_index": 15,
"book_page": 17,
"chapter": 2,
"dimensions": {
"width": 612,
"height": 792
},
"text_spans": [
{
"text": "Rights in Real Estate",
"font": "Arial-BoldMT",
"size": 27.04,
"bold": true,
"italic": false,
"bbox": {
"x0": 72,
"y0": 150,
"x1": 400,
"y1": 177
},
"color": 0,
"sequence": 1
}
],
"analysis": {
"font_sizes": {
"27.04": 1,
"11.04": 45
},
"font_styles": {
"bold_27.04": 1,
"regular_11.04": 45
},
"likely_headings": [
{
"text": "Rights in Real Estate",
"level": 1,
"confidence": 0.95
}
],
"likely_paragraphs": [
{
"text": "Real property consists of...",
"type": "body_text"
}
]
},
"extraction_timestamp": "2025-11-08T14:30:00Z",
"extraction_tool": "rich_extractor.py v1.0"
}
Python Commands to Execute
Step 1: Establish Page Mapping
cd Calypso/tools
python3 read_page_footers.py \
--start 15 \
--end 28 \
--pdf "../PREP-AL 4th Ed 9-26-25.pdf" \
--output "../analysis/page_mapping.json"
Success indicators:
- Command exits with code 0
- Page mapping JSON created/updated
- All pages in range have entries
Step 2: Extract Rich Data
cd Calypso/tools
python3 rich_extractor.py \
--pdf "../PREP-AL 4th Ed 9-26-25.pdf" \
--start 15 \
--end 28 \
--output "../analysis/chapter_02/rich_extraction.json"
Success indicators:
- Command exits with code 0
- JSON file created
- File contains text_spans array
- All pages in range represented
Step 3: Render to PNG
cd Calypso/tools
python3 -c "
import fitz
pdf = fitz.open('../PREP-AL 4th Ed 9-26-25.pdf')
for page_idx in range(15, 29):
page = pdf[page_idx]
pix = page.get_pixmap(matrix=fitz.Matrix(3, 3)) # 300% zoom for high-res
pix.save(f'../output/chapter_02/page_artifacts/page_{page_idx:02d}/02_page_{page_idx}.png')
pdf.close()
"
Step 4: Extract Images (if present)
cd Calypso/tools
# For each page with images
python3 extract_images.py \
--page 17 \
--pdf "../PREP-AL 4th Ed 9-26-25.pdf" \
--output "../output" \
--mapping "../analysis/page_mapping.json"
Quality Checks
Before declaring extraction complete:
File existence
-
01_rich_extraction.jsonexists -
02_page_XX.pngexists and is valid -
page_mapping.jsonexists
-
JSON validity
- JSON files parse without errors
- All required fields present
- No null/undefined values in critical fields
Data completeness
- All pages in range have text_spans
- Text content is not empty
- Font sizes are reasonable (> 0)
- Bounding boxes are within page dimensions
Image quality
- PNG files are readable
- Image dimensions match PDF page size
- No corrupted or blank images
Error Handling
If PDF file not found:
- Exit with error message
- Do not create partial artifacts
If page mapping fails:
- Fall back to default indexing (PDF index = book page - 1)
- Log warning
- Continue extraction
If rich extraction produces no text:
- Check if page is image-only
- Mark in metadata:
"page_type": "image_only" - Continue (ASCII preview will handle image OCR)
If PNG rendering fails:
- Use fallback: save raw PDF page as PDF image
- Log warning
- Continue to next step
Persistence & Traceability
All artifacts include metadata:
- Extraction timestamp
- Tool version
- Input parameters
- Processing status
This enables:
- Reproducibility (re-extract with same parameters)
- Debugging (trace what data was extracted)
- Auditing (track all changes to artifacts)
- Caching (skip re-extraction if unchanged)
Success Criteria
✓ All required files created in correct directories ✓ Rich extraction JSON is valid and complete ✓ PNG image renders correctly ✓ Page mapping is accurate ✓ All data persisted and ready for next skill ✓ No extraction errors or warnings
Next Steps
Once extraction completes successfully:
- Skill 2 will create ASCII preview from extracted data
- Skill 3 will use extraction + PNG + ASCII for HTML generation
- All artifacts available for validation and debugging
Troubleshooting
PDF won't open: Verify file path, ensure PDF is not corrupted No text extracted: Page may be image-only (OCR needed) Wrong page numbers: Check page_mapping.json for accuracy PNG images are blank: Try increasing zoom factor (3x = 300 DPI)
Implementation Notes
- This skill is fully deterministic - same inputs always produce same outputs
- Python tools ensure data quality and consistency
- All files saved to persistent storage for audit trail
- No AI involved at this stage - pure data extraction
- Ready to support later AI-based HTML generation with complete context