Open-source Rule-based PDF parser for RAG
The PDF parser is a rule based parser which uses text co-ordinates (boundary box), graphics and font data. The PDF parser works off text layer and also offers a OCR option to automatically use OCR if there are scanned pages in your PDFs. The OCR feature is based off a modified version of tika which uses tesseract underneath. The PDF Parser offers the following features: * Sections and subsections along with their levels. * Paragraphs - combines lines. * Links between sections and paragraphs. * Tables along with the section the tables are found in. * Lists and nested lists. * Join content…
Open-source Rule-based PDF parser for RAG launched on January 24, 2024 with 293 votes, #125 of 4,438 launches that month and more than 95% of that year's launches. The site was still online in September 2026, 2 years on.
In plain words
Open-source Rule-based PDF parser for RAG is a rule-based PDF parser designed for retrieval-augmented generation applications. It extracts structured content from PDFs by analyzing text coordinates, graphics, and font data, and includes OCR capabilities for scanned pages using a modified version of Tika with Tesseract. The parser identifies sections, subsections, paragraphs, tables, lists, and links while removing headers, footers, and watermarks. It is intended for developers and organizations that need to extract and structure PDF content for AI and machine learning workflows.
written from the facts on this page · September 2026
From the sources
This repo provides the server side code for llmsherpa API to connect. It includes parsers for various file formats. - nlmatics/nlm-ingestor
In the maker’s words, at launch
The PDF parser is a rule based parser which uses text co-ordinates (boundary box), graphics and font data. The PDF parser works off text layer and also offers a OCR option to automatically use OCR if there are scanned pages in your PDFs. The OCR feature is based off a modified version of tika which uses tesseract…
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