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 st
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Most enterprise data remains trapped in PDFs and documents that AI language models cannot directly use, requiring conversion to structured JSON format first. Two distinct problems exist under the phrase "PDF to JSON": schema-driven extraction, where fields are predefined and filled with values, and document parsing, where a model reconstructs the entire page into structured formats. Open-source models offer advantages over proprietary APIs by reducing costs, avoiding the need to send documents off-premise, and allowing local processing. Several open-source models and toolkits are available for each category, ranging from smaller vision-language models to document parsing systems that handle multiple file formats.
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