academic-poster-generator

name academic-poster-generator description Complete workflow for generating academic research posters from PDF literature; use when you need to extract paper content from PDFs and produce a LaTeX-based poster (beamerposter/tikzposter/baposter) with mandatory figure generation and a final rendered HTML deliverable. license MIT author AIPOCH Source : https://github.com/aipoch/medical-research-skills When to Use You have one or more research paper PDFs and need a conference-style poster draft with standard sections (Intro/Methods/Results/Conclusions). You want to automatically extract title/authors/abstract/sections from a PDF and restructure them into concise poster bullet points. You need a LaTeX poster scaffold using beamerposter , tikzposter , or baposter , with standard poster sizes (A0/A1/36×48"). You must ensure posters are visual-first (at least 2–3 generated figures) and pass an automated quality gate before delivery. You want a browser-viewable final output ( poster.rendered.html ) rather than a compiled PDF (PDF compilation is optional). Key Features End-to-end pipeline : PDF → metadata extraction → content structuring → figure generation → LaTeX poster assembly → HTML base conversion → agent rendering → final HTML. Scripted PDF processing : Convert PDF pages to images for reference. Extract metadata (title, authors, abstract, section structure). Structure content into poster-ready bullet points with word limits. Mandatory figure workflow : Generate at least 2–3 figures (schematics/flowcharts/mechanisms/comparison charts). Insert figures into LaTeX templates automatically or via config. Template support : assets/templates/beamerposter-template.tex assets/templates/tikzposter-template.tex assets/templates/baposter-template.tex HTML-first delivery policy : Final deliverables: poster.rendered.html + figures/ Intermediate artifacts are temporary and must not be returned. Quality control : Automated checks for HTML/LaTeX outputs. Manual checklist reference: references/poster_quality_checklist.md Design guidance : Poster design principles: references/design_principles.md Dependencies Python (recommended) Python >=3.8 pypdf (version not pinned) pdfplumber (version not pinned) pdf2image (version not pinned) pytesseract (version not pinned; required for OCR on scanned PDFs) pandas (version not pinned; optional for table handling) Pillow (version not pinned) matplotlib (version not pinned) Install (example): pip install pypdf pdfplumber pdf2image pytesseract pandas pillow matplotlib System tools tesseract-ocr (for OCR; required only for scanned PDFs) Poppler utilities (commonly required by pdf2image , e.g., pdftoppm ) LaTeX (optional, only if compiling PDF) TeX Live / MiKTeX / MacTeX TeX packages (via tlmgr , names may vary by distribution): beamerposter tikzposter baposter qrcode xcolor tcolorbox subcaption graphics Example: tlmgr install beamerposter tikzposter baposter qrcode xcolor tcolorbox subcaption graphics HTML conversion pandoc (required for --html-only base HTML generation) pdf2htmlEX (optional; only if doing PDF → HTML rendering) Example Usage 1) Run the end-to-end pipeline (recommended) Creates a timestamped directory under output/ and supports HTML-only mode. python scripts/run_poster_pipeline.py paper.pdf --html-only Expected final structure: output/YYYYMMDD_HHMMSS/ ├── poster.rendered.html └── figures/ ├── mechanism.png ├── process_flowchart.png └── comparison_chart.png 2) Run stages manually (fully reproducible) Stage A — Extract metadata and structure content (temporary artifacts) python scripts/extract_metadata.py paper.pdf metadata.json python scripts/structure_content.py paper.pdf --json poster_content.json --latex content.tex --max-words 800 python scripts/convert_pdf_to_images.py paper.pdf paper_images/ --dpi 600 Stage B — Generate figures (mandatory) At least 2–3 figures are required. python scripts/generate_figures.py schematic "Cell signaling pathway" mechanism.png python scripts/generate_figures.py flowchart "Step 1;Step 2;Step 3" process_flowchart.png python scripts/generate_figures.py comparison '{"Control":[65],"Our Method":[87]}' comparison_chart.png Or generate from a config: python scripts/generate_figures.py config figures_config.json Stage C — Create poster LaTeX from a template (temporary artifact) cp assets/templates/beamerposter-template.tex poster.tex # (Edit poster.tex: title/authors/institute + paste structured content) Stage D — Insert figures into LaTeX (temporary artifact) python scripts/insert_figures.py poster.tex --all # or: python scripts/insert_figures.py poster.tex --config figures_config.json Stage E — Convert to base HTML and render final HTML (final artifact) python scripts/convert_poster.py poster.tex --html-only # Agent step: read poster.html, validate <img> paths, inject CSS/layout, output poster.rendered.html Stage F — Quality control (mandatory) python scripts/check_poster_quality.py poster.rendered.html Implementation Details Pipeline and file policy All generated files must be placed under output/ (preferably a timestamped subdirectory). Only the following are considered final deliverables : poster.rendered.html figures/ directory (PNG figures) All other files are intermediate/temporary and must not be returned to the user, including (non-exhaustive): poster.tex , poster.html metadata.json , poster_content.json , figures_config.json , figures.json LaTeX auxiliary files ( .aux , .log , .out , etc.) Conceptual flow: PDF → metadata extraction (temp) → content structuring (temp) → figure generation (final figures/) → LaTeX poster (temp) → base HTML (temp) → agent rendering → poster.rendered.html (final) PDF extraction approach Text and tables are extracted via pdfplumber . For scanned PDFs, OCR is performed by converting pages to images ( pdf2image ) and running pytesseract . Typical extraction targets: Title/authors (first-page patterns) Abstract (from “Abstract” header to next section) Section blocks (Introduction/Methods/Results/Conclusion) Tables (converted to summaries for poster bullets) Content structuring rules Convert paragraphs to bullet points suitable for poster blocks. Enforce a poster-friendly word budget (commonly 600–800 words total). Prefer 3–6 key visuals; summarize tables into a small set of metrics. Figure requirements (mandatory) Every poster must include at least 2–3 generated figures . Target 40–50% of poster area as visual content. Figures should be ≥300 DPI , with clear labels and concise captions. Supported figure categories: Schematics (systems, pathways, conceptual diagrams) Flowcharts (procedures, pipelines, algorithms) Mechanism diagrams (biological/chemical processes) Comparison charts (bar charts, benchmarks) LaTeX package selection Use one of the supported poster packages depending on style needs: beamerposter (traditional academic) tikzposter (modern, colorful) baposter (multi-column layouts) Templates are provided in assets/templates/ . HTML rendering requirements (agent step) After pandoc produces poster.html , the renderer must: Verify all <img> references exist and paths resolve to figures/ . Apply poster-grade CSS (grid columns, typography, spacing, captions). Ensure accessibility: WCAG AA contrast ≥ 4.5:1 . Output poster.rendered.html as the only final HTML artifact. Minimum layout expectations: 2–3 column grid on desktop; single column on narrow screens. Clear hierarchy: title/authors/institution, section headers, bullet lists, figure blocks with captions. Quality control requirements (mandatory) Run scripts/check_poster_quality.py on the final output: Validate that images exist and render correctly. Confirm layout integrity (columns/blocks). Ensure readability (font sizes, spacing). Confirm contrast compliance (≥ 4.5:1). Confirm figure count (≥ 2–3) and that no placeholder text remains. References: Design principles: references/design_principles.md Manual checklist: references/poster_quality_checklist.md