opendataloader-project/opendataloader-pdf

OpenDataLoader PDF is an open-source PDF parser with two main jobs. The first is turning a PDF into structured data that an AI pipeline can use. The second is improving PDF accessibility by adding the hidden tagging that screen readers rely on to a PDF that does not already have it. For data extraction, the tool reads a PDF and outputs Markdown, JSON with bounding boxes for every element, or HTML. Each heading, list, table, and image is detected along with its coordinates on the page, and a reading-order algorithm called XY-Cut++ keeps the flow correct on multi-column or complicated layouts. A local mode runs deterministically and quickly, while an optional hybrid mode routes harder pages to an AI backend to handle complex or borderless tables, scanned PDFs through built-in OCR for 80+ languages, LaTeX formulas, and AI-written descriptions of charts and images. The project reports a 0.907 overall and 0.928 table accuracy on its own benchmark of 200 real-world PDFs. For accessibility, the free Apache-2.0 core takes an untagged PDF and produces a Tagged PDF following the Well-Tagged PDF specification, validated with veraPDF and built in collaboration with the PDF Association and Dual Lab. A paid enterprise add-on converts that Tagged PDF further to the PDF/UA-1 or PDF/UA-2 standards and ships an accessibility studio with a visual editor. Teams reach for it when feeding a retrieval-augmented generation system from PDFs, or when remediating accessibility at scale rather than paying for manual fixes per document, which the README says typically run $50 to $200 each. It ships as a Java 11+ tool with Python, Node.js, and Java SDKs, a LangChain integration, and prompt-injection and header/footer filters built in. It does not process Word, Excel, or PowerPoint files and does not need a GPU.