pptspine

A pure-Rust PowerPoint (.pptx) parser with Python bindings (PyO3 / maturin, abi3-py311). A .pptx file is OOXML — a zip archive of XML parts — and pptspine walks that XML directly to produce a structured, information-preserving model: slides, text frames (paragraphs + styled runs), tables (cells, merges, fills), pictures, and autoshapes. A parsed deck can also be exported to PDF (to_pdf() / save_pdf(), one page per slide) through the shared pure-Rust pdf-typeset engine from the sibling pdfspine — no LibreOffice, no cloud converter. Embedded images can additionally be OCR'd locally, offline, and deterministically via the sibling ocrspine crate (PP-OCRv5 through tract-onnx — no cloud, no network).

Capabilities

Parsing is tolerant: unknown elements are skipped, missing attributes become None, and malformed input yields a typed PptError rather than a panic.

Install

pip install pptspine

pptspine is on PyPI. OCR works out of the box: the PP-OCRv5 weights ship in the shared ocrspine-models data package — a runtime dependency pip pulls in automatically — so the wheel itself ships no models. To build from source instead, see below.

Build (from the package root)

uv venv .venv
VIRTUAL_ENV="$(pwd)/.venv" uv pip install maturin pytest
# Structural parsing needs no models. The OCR path resolves models from
# ../ocrspine/models by default (or set OCRSPINE_MODELS).
OCRSPINE_MODELS="$(cd ../ocrspine && pwd)/models" \
  VIRTUAL_ENV="$(pwd)/.venv" .venv/bin/maturin develop --release

Use from Python

import pptspine

pres = pptspine.open("deck.pptx")
print(pres.slide_count, pres.slide_size)   # e.g. 2 (9144000, 6858000)  # EMU

for slide in pres.slides():
    for shape in slide.shapes():           # list[dict], introspectable
        if shape["kind"] == "text":
            for para in shape["paragraphs"]:
                for run in para["runs"]:
                    print(run["text"], run["bold"], run["color"])
        elif shape["kind"] == "table":
            for row in shape["rows"]:
                print([cell["text"] for cell in row])
        elif shape["kind"] == "picture":
            print("image:", shape["media"])

# Structured export + speaker notes:
print(pres.to_text())          # slides joined by "--- slide N ---"
print(pres.to_markdown())      # one section per slide; GFM / HTML tables
print(pres.slides()[0].text)   # all text on a slide (convenience)
print(pres.slides()[0].notes)  # speaker notes, or None

# Run OCR on raw image bytes (PNG/JPEG), offline:
items = pptspine.ocr_image(open("scan.png", "rb").read())
print(" ".join(i["text"] for i in items))

# End-to-end: pull an embedded image's bytes and OCR them, offline:
for shape in pres.slides()[0].shapes():
    if shape["kind"] == "picture" and shape["media"]:
        data = pres.image_bytes(shape["media"])   # bytes | None
        if data:
            print([i["text"] for i in pptspine.ocr_image(data)])

Export to PDF

pres = pptspine.open("deck.pptx")
pres.save_pdf("deck.pdf")          # one PDF page per slide
pdf_bytes = pres.to_pdf()          # or in-memory bytes

# Optional: map a requested font family to a local font file (or to another
# installed family), layered on top of the built-in substitution table:
pres.save_pdf("deck.pdf", font_map={"Aptos": "/path/to/Aptos.ttf"})

Rendering is deterministic and fully offline. Missing fonts degrade gracefully: an available face is substituted and a Python UserWarning is emitted once per warning kind — the export never fails on a missing font.

Rust workspace

crates/
  ppt-core    domain model + geometry (EMU) + typed PptError. No IO/zip/XML.
  ppt-parse   OOXML reader: zip extract + quick-xml walk -> Presentation.
  ppt-ocr     image-OCR bridge over ocrspine (PaddleOcr).
  ppt-render  slide -> PDF renderer over the shared pdf-typeset engine (from pdfspine).
  py-bindings PyO3 _core extension (the FFI chokepoint).

Deferred / follow-up

• Image-table geometry reconstruction from OCR boxes (ppt_ocr::reconstruct_table_from_image, currently a typed Unsupported stub).
• Richer color models (gradients), hyperlinks, charts, SmartArt, comments.