Reflow

An Obsidian plugin that converts a PDF into clean, readable Markdown entirely on your device. Figures, tables, and math survive. Nothing uploads.

Right-click a PDF in your vault → Convert to Markdown. A vision model runs locally on your GPU and writes a Markdown package next to the source:

1706.03762v7.pdf
1706.03762v7/
  1706.03762v7.md     # frontmatter, headings, tables, $LaTeX$ math, figure links
  images/             # extracted figures
  meta.json           # engine, timings, warnings

No API key, no account, no page limit, and the document never leaves your machine to be converted. A two-column paper becomes one reflowable column in your own typography — which is the point of the name.

Using it

• Convert to Markdown in a PDF's right-click menu, or the Convert active PDF to Markdown command.
• A progress dialog shows the page, live token count, elapsed time and estimate. Close it to keep reading — conversion carries on and moves to the status bar.
• Multiple conversions can run at the same time with separate progress in the status bar.
• Settings: output folder, page limit, per-page time limit, compute backend, and whether to convert on a background thread.

Desktop only, and a WebGPU-capable machine is strongly preferred: without one the plugin falls back to the CPU, says so in red, and takes minutes per page. The first conversion downloads about 1 GB of model weights from Hugging Face and caches them — that and two pinned CDN assets are the only network use, itemised in plugin/README.md.

Note: Windows and Linux are untested.

Thesis

Markdown is the native format AI consumes and generates, and it is quietly becoming the best reading format humans own: reflowable, themeable, searchable, yours. Meanwhile the science of PDF → structured text is largely solved in open source (Docling, Marker, MinerU, Granite-Docling) — but it is packaged for developers, not humans.

The product: a converter good enough that someone who read the PDF would rather read the Markdown.

Two benefits fall out:

1. Normalization. Every paper — regardless of journal, era, or layout idiosyncrasy — becomes the same clean, reflowable reading surface in your typography and theme. Headings become a real outline/TOC. Figures sit inline. Math renders (Obsidian renders LaTeX natively).
2. Ownership + privacy. The document never leaves your machine to be converted. Confidential docs (legal, medical, unpublished work) stay confidential.

The open niche

PDF → Markdown is crowded — but every existing option gives up one of effortless, complete (figures + LaTeX + tables), or local:

Option	Effortless	Complete	Local
Mathpix ($4.99/mo)	✅	✅	❌ cloud, 10 free pages/mo
Obsidian AI plugins (Marker/Mistral/GPT)	~ (API keys)	✅	❌ upload, or self-host a Python server
Markitdown / heuristic tools	✅	❌ naive extraction	✅
OSS engines (Docling, Marker CLI)	❌ Python env, flags	✅	✅
This project	✅	✅	✅

Install

Not yet in the community directory. Until then, build it yourself:

cd plugin && npm install && npm run deploy

then enable Reflow in Settings → Community plugins. Full build and development instructions: plugin/README.md.

Architecture (target)

PDF ─┬─ fast tier: text-layer extract + layout heuristics (clean PDFs)
     └─ accurate tier: layout + table + OCR + formula models
              │
              ▼
   Structured document IR (JSON: typed blocks, provenance page/bbox,
   formula nodes carry LaTeX payloads)
              │
   ┌──────────┼──────────────┐
   ▼          ▼              ▼
 Markdown   EPUB          one-pager (later)
 + images/  (per-device
 (canonical) math: MathML
             or image)

• Engine, target (primary): a portable TypeScript + ONNX core in engine-js/ — a single compact VLM (granite-docling-258M, official ONNX export, Apache-2.0) running under transformers.js. Full page image → DocTags → Markdown, entirely in JS: no Python, no sidecar binary. The same core embeds in a Node CLI, an Obsidian desktop plugin, a browser extension, and eventually mobile — only device/dtype change. Rationale and route comparison: docs/perf-and-portability.md.
• Engine, bootstrap (reference oracle): Python Docling (MIT) — the fast path that answered is the quality there? and froze the artifact contract + fixture suite. Retained as the modular fallback and a numeric cross-check for the VLM (it copies table cells from the PDF text layer; the VLM can invent them). Not on the shipping path.
• Math policy: LaTeX-first ($...$) — Obsidian renders it natively; images only as low-confidence fallback and for e-ink EPUB export.
• Never silently wrong (VLM hedge): VLM-emitted numeric table cells are reconciled against the pdf.js text layer; the fixture numeric-fidelity checks are the arbiter before any quantized build becomes default.