opendataloader-project
OpenDataLoader Project is an AI-powered, open-source SDK for extracting structured, AI-ready data from PDFs and other documents. Its focus is fast, accurate, local-first document processing for RAG, document analysis, automation, and information retrieval, including accessibility auto-tagging.
At a Glance
- Developers building RAG, agentic AI and document-AI applications
- Enterprises with private or on-premises document-processing requirements
- Organizations processing large PDF collections for AI training or retrieval
- Public institutions and enterprises needing accessibility compliance
- +1 more
AI Tools by opendataloader-project
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OpenDataLoader PDF
Open Source PDF Parser for LLMs
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Latest News
OpenDataLoader PDF v2.5.13 released with auto-tagging heading normalization, veraPDF updates, hybrid fixes and publishing improvements.
OpenDataLoader PDF v2.5.12 released with hybrid picture-description handling, improved scanned-page routing, table-header detection and OCR documentation.
OpenDataLoader PDF v2.5.11 released with diagnosable hybrid-server behavior, structured logging and configurable backend page batching.
OpenDataLoader PDF v2.5.10 released with snapshot publishing, auto-tagging maintenance and CI improvements.
Products & Services
Open-source local PDF parser that converts PDFs to structured Markdown, JSON and HTML with semantic element types, reading order, tables, lists, images and bounding boxes. It supports Python, Node.js and Java interfaces and is distributed under Apache-2.0.
Optional hybrid pipeline that combines deterministic local extraction with AI backends for scanned PDFs, OCR, complex or borderless tables, formulas, charts and image descriptions.
Open-source pipeline that analyzes untagged PDFs and writes structure tags to produce Tagged PDFs, with validation based on veraPDF and the Well-Tagged PDF specification.
Hancom’s enterprise document-analysis platform integration, positioned as a hybrid backend/partner capability. It adds VLM-powered chart and image understanding, customer-customized AI models and native HWP/HWPX support, alongside OpenDataLoader’s local routing and processing.
Market Position
OpenDataLoader positions itself as a local-first, CPU-capable, open-source alternative to PDF extraction tools such as Docling, Marker, Unstructured, MinerU, PyMuPDF4LLM, MarkItDown, EdgeParse and commercial tools such as Nutrient. It differentiates through deterministic reading order, bounding boxes for citations, structured table output, hybrid AI enrichment, built-in safety filtering and open-source PDF accessibility auto-tagging. Its published ODL-Bench results report a 0.907 overall score for hybrid mode, ranked first in the comparison shown on its site.
Founding Story
Hancom created OpenDataLoader to address the difficulty of extracting useful AI-training and retrieval data from PDFs, whose complex internal structure often destroys reading order, tables, layout and source coordinates. The project was jointly developed with Dual Lab and released globally as open source so developers could build private, interoperable document-AI workflows.
Business Model
Revenue Model
Open-core model: the PDF layout-analysis, extraction and auto-tagging capabilities are available as free Apache-2.0 software, while Hancom positions PDF/UA-compliant conversion, an accessibility workspace and future proprietary AI add-ons as paid enterprise offerings.
Pricing Tiers
Local PDF extraction and auto-tagging under Apache-2.0; the accessibility page says there are no per-page fees for the open-source core.
Commercial conversion of Tagged PDFs to PDF/UA-1 or PDF/UA-2 compliant output.
Commercial visual workspace for reviewing, adjusting and approving accessibility tags before export.
Target Markets
- Developers building RAG, agentic AI and document-AI applications
- Enterprises with private or on-premises document-processing requirements
- Organizations processing large PDF collections for AI training or retrieval
- Public institutions and enterprises needing accessibility compliance
- AI, data, software and information-retrieval teams
- RAG and LLM knowledge pipelines
- AI training-data extraction
- Document search, information retrieval and citation generation
- Enterprise document analysis and automation
- Processing scanned PDFs, complex tables, formulas and charts
- PDF accessibility remediation and Tagged PDF workflows