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Structured PDF-to-JSON: A Guide to Open-Source Extraction Models in 2026

Most enterprise data still sits inside PDFs, scans, and slide decks. Large language models and agents cannot use that data until it becomes structured JSON. Open-source document extraction has become the standard way to do that conversion on your own hardware. Two different problems hide under the phrase ‘PDF to JSON.’ The first is schema-driven extraction: you define fields, and a model fills them with values. The second is document parsing: a model reconstructs the page into structured JSON or Markdown. Most teams need one, sometimes both. Choosing the wrong category costs real time. This guide evaluates key open-source models and toolkits in 2026, grouped by their actual function.

SourceMarkTechPostAuthor: Michal Sutter
Structured PDF-to-JSON: A Guide to Open-Source Extraction Models in 2026
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  • Open-source document extraction splits into schema-driven extraction (fill predefined fields) and document parsing (rebuild page layout to JSON).
  • Key models include Datalab lift (schema-driven, 9B, 90.2% field accuracy), NuMind NuExtract 3 (4B, combines OCR and extraction), IBM Docling (layout parsing, MIT license), among others.
  • Licensing varies widely: lift uses Apache-2.0 code with modified OpenRAIL-M weights, MinerU moved from AGPL-3.0 to a custom license, olmOCR 2 is fully Apache-2.0.
  • Published benchmarks come from different suites and are not directly comparable; test models on your own documents.

Highlights and analysis are generated automatically and may contain errors. Check the original source.

Structured PDF-to-JSON: A Guide to Open-Source Extraction Models in 2026 | AI News Hub