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OpenDataLoader PDF: Parse 100+ Pages per Second Without a GPU
An open-source approach to extracting structured document content and generating accessible, tagged PDFs on ordinary hardware.
Organizations have millions of documents locked inside PDFs: contracts, research papers, financial reports, manuals, invoices, and government forms. PDFs preserve how a page looks, but that does not mean they preserve how its content should be understood. Headings, tables, reading order, and relationships between sections can be difficult to recover, especially in two-column papers, complex layouts, or scanned documents. Extracting the text is easy; extracting its structure reliably is much harder.
That distinction matters for modern AI systems. Retrieval-augmented generation (RAG), document search, summarization, and agent workflows all depend on the quality of the content they receive. If a table loses its relationships, a heading becomes detached from its section, or a scanned page produces no usable text, the errors can propagate through the entire pipeline. Better models cannot reliably compensate for information that was lost during extraction.
OpenDataLoader PDF tackles this problem at the document-processing layer. It is an open-source parser designed to convert PDFs into structured Markdown, JSON with bounding-box information, and HTML. It runs on a CPU…
