From academic-skills
Extracts figures and sub-figures from academic PDF papers. Supports multiple figure types (Fig, Scheme, Supplementary Figure, Extended Data) and sub-figure labels. Outputs high-quality PNG at configurable DPI.
How this skill is triggered — by the user, by Claude, or both
Slash command
/academic-skills:sci-figureThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Precisely extract figures and sub-figures from academic PDF papers.
CHANGELOG.mdLICENSEREADME.mdREADME_CN.md_meta.jsonrequirements.txtsci_figure.egg-info/PKG-INFOsci_figure.egg-info/SOURCES.txtsci_figure.egg-info/dependency_links.txtsci_figure.egg-info/entry_points.txtsci_figure.egg-info/requires.txtsci_figure.egg-info/top_level.txtsci_figure/__init__.pysci_figure/annotator.pysci_figure/caption_detector.pysci_figure/cli.pysci_figure/column_detector.pysci_figure/exceptions.pysci_figure/figure_extractor.pysci_figure/image_processor.pyPrecisely extract figures and sub-figures from academic PDF papers.
License note: sci-figure is licensed under AGPL-3.0-or-later because it links PyMuPDF (fitz), which is AGPL-licensed.
Install the package from the skill directory before first use:
cd ${SKILL_DIR}
pip install -e .
This registers the sh-sci-fig CLI command. Requires Tesseract OCR:
winget install UB-Mannheim.TesseractOCRapt install tesseract-ocrbrew install tesseractUse Bash to check EXTEND.md existence (priority order):
# Check project-level first
test -f .baoyu-skills/sci-figure/EXTEND.md && echo "project"
# Then user-level (cross-platform: $HOME works on macOS/Linux/WSL)
test -f "$HOME/.baoyu-skills/sci-figure/EXTEND.md" && echo "user"
EXTEND.md Supports: Default DPI | Default output format | Tesseract path
sh-sci-fig <input.pdf> [options]
| Option | Short | Description | Default |
|---|---|---|---|
<input> | PDF file path | Required | |
--figure | -f | Figure number (1, 2, 3...) | Required (except --list/--all) |
--subfigure | -s | Sub-figure label (a, b, c...) | None (returns whole figure) |
--output | -o | Output directory | Current directory |
--dpi | -d | Output resolution | 600 |
--list | -l | List all available figure numbers | false |
--all | Extract all figures | false | |
--format | Output format (png/jpg) | png | |
--strategy | Extraction strategy: hybrid/native/cv | hybrid | |
--ocr | OCR engine: tesseract/easyocr/none | tesseract | |
--render-page | Render full page with annotations | false | |
--annotate | Draw bounding boxes on rendered page | false | |
--bbox | Manual bbox override (x0,y0,x1,y1 in px) | None | |
--no-trim | Disable whitespace trimming | false | |
--debug | Enable debug logging | false | |
--quiet | -q | Suppress info messages | false |
# Extract Figure 2, sub-figure c
sh-sci-fig paper.pdf -f 2 -s c
# Extract entire Figure 3
sh-sci-fig paper.pdf -f 3
# List all available figures in a PDF
sh-sci-fig paper.pdf --list
# Extract all figures
sh-sci-fig paper.pdf --all
# Custom output directory and DPI
sh-sci-fig paper.pdf -f 2 -s c -o ./output/ -d 300
# Use EasyOCR for sub-figure label detection
sh-sci-fig paper.pdf --all --ocr easyocr
# CV-only strategy (skip native extraction)
sh-sci-fig paper.pdf --all --strategy cv
# Render page with annotated bounding boxes (debugging)
sh-sci-fig paper.pdf -f 1 --render-page --annotate
# Manual bbox extraction (multimodal correction)
sh-sci-fig paper.pdf -f 1 --bbox 100,200,800,1200
Output:
Extracted: figure_2c.png (1920x1080, 600 DPI)
| Scenario | Behavior |
|---|---|
| Figure number not found | Error + list all available figure numbers |
| OCR recognition failed | Return entire figure region |
| Sub-figure split failed | Return entire figure region |
| No sub-figure labels found | Return entire figure region |
| Library | Role |
|---|---|
| pdfplumber | Text + coordinate extraction (caption detection) |
| PyMuPDF (fitz) | Native image extraction + high-quality page rendering |
| opencv-python | CV region detection, connected-component analysis, content validation |
| Pillow | Final cropping, format conversion |
| pytesseract | OCR for sub-figure label recognition (default) |
| easyocr | Alternative OCR engine (optional, pip install sci-figure[ocr]) |
| numpy | Image array operations |
| Engine | Priority | Best For |
|---|---|---|
| Native (PyMuPDF) | 1st | Raster images embedded in PDF |
| CV (connected-component) | 2nd | Vector graphics, colored plots |
| Caption-anchored | 3rd | Fallback when above engines fail |
The hybrid strategy (default) tries all three in order and validates results.
Each figure returned by FigureExtractor.detect_all() is a dict with these keys:
| Field | Type | Description |
|---|---|---|
number | int | Figure number |
page | int | Page index (0-based) |
bbox_pdf | tuple | Crop region in PDF points (x0, y0, x1, y1) |
bbox_px | tuple | Crop region in pixels (x0, y0, x1, y1) |
caption_text | str | Full caption text |
figure_type | str | One of: figure, scheme, chart, supplementary, extended_data |
sublabels | list[str] | Sub-figure labels, e.g. ["a","b","c"] |
image | ndarray | Cropped figure image (numpy array) |
engine_used | str | Engine that produced the crop: native, cv, or fallback |
list_figures() returns the same dicts without the image field.
Custom configurations via EXTEND.md. See Preferences section for paths and supported options.
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