Dense PDF to LLM Text and Page-Aligned Markdown

Problem

Scanned, table-dense, or layout-heavy PDFs are often not ready for LLMs. Naive text extraction can produce disorder, missing page context, and unstable block boundaries, which hurts chunking, retrieval, entity extraction, and citation workflows. This skill frames the conversion as a reusable preprocessing step instead of leaving agents to assemble ad hoc parsers.

How It Works

The skill centers on olmOCR and targets a simple path: scanned or layout-complex PDFs to LLM-ready text or page-aligned Markdown. Core capabilities include:
- Clean text output for chunking, retrieval, and prompt construction.
- Page-aligned Markdown, preserving page boundaries, reading order, and citation anchors.
- Downstream-ready output for search, extraction, and citation workflows rather than one-off OCR dumps.

It relies on PDF rendering and vision-language-model understanding. The materials note poppler-utils plus extra fonts for rendering PDF images; local inference can use an NVIDIA GPU, and the upstream model is described as a 7B vision-language model, making GPU deployment the realistic target. The processing path can be read as: render PDF pages to images -> olmOCR interprets layout and text -> output LLM-ready text or page-aligned Markdown.

Boundaries

It is more useful for scanned or layout-heavy documents where page structure matters. If the goal is merely to copy plain text, a lightweight text extractor may be enough. Because the output feeds downstream LLM tasks, check whether chunks are too fragmented, page alignment is stable, and tables or equations are not misjoined. Model inference and font rendering make the runtime heavier than plain text conversion, so allow time to debug layout-parsing edge cases.