@AIExplorerTim: Someone just released a tool that converts PDFs into clean, structured Markdown at speeds up to 100 pages/second. No GPU required. No API costs. No messy parsing. Just raw, usable data. It handles with ease: • Tables → Perfectly ex…

Someone just released a tool that converts PDFs into clean, structured Markdown at 100 pages/second. No GPU required. No API costs. No messy parsing. Just raw, usable data. It handles with ease: • Tables → perfectly extracted • Broken layouts → auto-repaired • Nested data → structurally cleaned • Scanned clutter → converted to readable text. This isn’t a minor upgrade. It eliminates 90% of manual data cleanup overnight. The tool is called OpenDataLoader, and… it’s open-source. Repository → https://t.co/Jtg3bo3LD2 — # opendataloader-project/opendataloader-pdf Source: https://github.com/opendataloader-project/opendataloader-pdf # OpenDataLoader PDF PDF Parser for AI-ready data. Automate PDF accessibility. Open-source. License (https://github.com/opendataloader-project/opendataloader-pdf/blob/main/LICENSE) PyPI version (https://pypi.org/project/opendataloader-pdf/) npm version (https://www.npmjs.com/package/@opendataloader/pdf) Maven Central (https://search.maven.org/artifact/org.opendataloader/opendataloader-pdf-core) Java (https://github.com/opendataloader-project/opendataloader-pdf#java) 🔍 PDF parser for AI data extraction — Extract Markdown, JSON (with bounding boxes), and HTML from any PDF. #1 in benchmarks (0.907 overall). Deterministic local mode + AI hybrid mode for complex pages. - How accurate is it? — #1 in benchmarks: 0.907 overall, 0.928 table accuracy across 200 real-world PDFs including multi-column and scientific papers. Deterministic local mode + AI hybrid mode for complex pages (benchmarks) - Scanned PDFs and OCR? — Yes. Built-in OCR (80+ languages) in hybrid mode. Works with poor-quality scans at 300 DPI+ (hybrid mode) - Tables, formulas, images, charts? — Yes. Complex/borderless tables, LaTeX formulas, and AI-generated picture/chart descriptions all via hybrid mode (hybrid mode) - How do I use this for RAG? — pip install opendataloader-pdf, convert in 3 lines. Outputs structured Markdown for chunking, JSON with bounding boxes for source citations, and HTML. LangChain integration available. Python, Node.js, Java SDKs (quick start | LangChain) ♿ PDF accessibility automation — Auto-tag untagged PDFs into screen-reader-ready Tagged PDFs at scale. First open-source tool to generate Tagged PDFs end-to-end. - What’s the problem? — Accessibility regulations are now enforced worldwide. Manual PDF remediation costs $50–200 per document and doesn’t scale (regulations) - What’s free? — Layout analysis + auto-tagging (Apache 2.0). Untagged PDF in → Tagged PDF out. No proprietary SDK dependency (auto-tagging) - What about PDF/UA compliance? — Converting Tagged PDF to PDF/UA-1 or PDF/UA-2 is an enterprise add-on. Auto-tagging generates the Tagged PDF; PDF/UA export is the final step (pipeline) - Why trust this? — Built in collaboration with Dual Lab (https://duallab.com) (veraPDF (https://verapdf.org) developers) based on PDF Association (https://pdfa.org) specifications, best practice guides and expertise of the PDF Community (https://pdfa.org/community/). Auto-tagging follows the Well-Tagged PDF specification (https://pdfa.org/wtpdf/), validated with veraPDF (collaboration (https://opendataloader.org/docs/tagged-pdf-collaboration)) ## Get Started in 30 Seconds Requires: Java 11+ and Python 3.10+ (Node.js (https://opendataloader.org/docs/quick-start-nodejs) | Java (https://opendataloader.org/docs/quick-start-java) also available) > Before you start: run java -version. If not found, install JDK 11+ from Adoptium (https://adoptium.net/). bash pip install -U opendataloader-pdf python import opendataloader_pdf # Batch all files in one call — each convert() spawns a JVM process, so repeated calls are slow opendataloader_pdf.convert( input_path=["file1.pdf", "file2.pdf", "folder/"], output_dir="output/", format="markdown,json" ) OpenDataLoader PDF layout analysis — headings, tables, images detected with bounding boxes Annotated PDF output — each element (heading, paragraph, table, image) detected with bounding boxes and semantic type. ## What Problems Does This Solve? | Problem | Solution | Status | |———|–––––|––––| | PDF structure lost during parsing — wrong reading order, broken tables, no element coordinates | Deterministic local PDF to Markdown/JSON with bounding boxes, XY-Cut++ reading order | Shipped | | Complex tables, scanned PDFs, formulas, charts need AI-level understanding | Hybrid mode routes complex pages to AI backend (#1 in benchmarks) | Shipped | | Manual PDF remediation cost — Accessibility regulations (EAA, ADA, Section 508) demand Tagged PDFs. Manual remediation costs $50–200/doc | Auto-tag untagged PDFs into Tagged PDFs (free, Apache 2.0). Foundation for PDF/UA workflows; full PDF/UA-1/2 export is an enterprise add-on | Auto-tag: Shipped. PDF/UA export: Enterprise | ## Capability Matrix | Capability | Supported | Tier | |————|———–|——| | Data extraction | | | | Extract text with correct reading order | Yes | Free | | Bounding boxes for every element | Yes | Free | | Table extraction (simple borders) | Yes | Free | | Table extraction (complex/borderless) | Yes | Free (Hybrid) | | Heading hierarchy detection | Yes | Free | | List detection (numbered, bulleted, nested) | Yes | Free | | Image extraction with coordinates | Yes | Free | | AI chart/image description | Yes | Free (Hybrid) | | OCR for scanned PDFs | Yes | Free (Hybrid) | | Formula extraction (LaTeX) | Yes | Free (Hybrid) | | Tagged PDF structure extraction | Yes | Free | | AI safety (prompt injection filtering) | Yes | Free | | Header/footer/watermark filtering | Yes | Free | | Accessibility | | | | Auto-tagging → Tagged PDF for untagged PDFs | Yes | Free (Apache 2.0) | | PDF/UA-1, PDF/UA-2 export | 💼 Available | Enterprise | | Accessibility studio (visual editor) | 💼 Available | Enterprise | | Limitations | | | | Process Word/Excel/PPT | No | — | | GPU required | No | — | ## Extraction Benchmarks opendataloader-pdf [hybrid] ranks #1 overall (0.907) across reading order, table, and heading extraction accuracy. | Engine | Overall | Reading Order | Table | Heading | Speed (s/page) | License | |––––|———|—————|—––|———|––––––––|———| | opendataloader [hybrid] | 0.907 | 0.934 | 0.928 | 0.821 | 0.463 | Apache-2.0 | | nutrient | 0.885 | 0.925 | 0.708 | 0.819 | 0.008 | Commercial | | docling | 0.882 | 0.898 | 0.887 | 0.824 | 0.762 | MIT | | marker | 0.861 | 0.890 | 0.808 | 0.796 | 53.932 | GPL-3.0 | | unstructured [hi_res] | 0.841 | 0.904 | 0.588 | 0.749 | 3.008 | Apache-2.0 | | edgeparse | 0.837 | 0.894 | 0.717 | 0.706 | 0.036 | Apache-2.0 | | opendataloader | 0.831 | 0.902 | 0.489 | 0.739 | 0.015 | Apache-2.0 | | mineru | 0.831 | 0.857 | 0.873 | 0.743 | 5.962 | AGPL-3.0 | | pymupdf4llm | 0.732 | 0.885 | 0.401 | 0.412 | 0.091 | AGPL-3.0 | | unstructured | 0.686 | 0.882 | 0.000 | 0.388 | 0.077 | Apache-2.0 | | markitdown | 0.589 | 0.844 | 0.273 | 0.000 | 0.114 | MIT | | liteparse | 0.576 | 0.866 | 0.000 | 0.000 | 1.061 | Apache-2.0 | > Scores normalized to [0, 1]. Higher is better for accuracy; lower is better for speed. Bold = best. Full benchmark details (https://github.com/opendataloader-project/opendataloader-bench) Benchmark (https://github.com/opendataloader-project/opendataloader-bench) Quality Breakdown (https://github.com/opendataloader-project/opendataloader-bench) ## Which Mode Should I Use? | Your Document | Mode | Install | Server Command | Client Command | |—————|——|———|––––––––|––––––––| | Standard digital PDF | Fast (default) | pip install opendataloader-pdf | None needed | opendataloader-pdf file1.pdf file2.pdf folder/ | | Complex or nested tables | Hybrid | pip install "opendataloader-pdf[hybrid]" | opendataloader-pdf-hybrid --port 5002 | opendataloader-pdf --hybrid docling-fast file1.pdf file2.pdf folder/ | | Scanned / image-based PDF | Hybrid + OCR | pip install "opendataloader-pdf[hybrid]" | opendataloader-pdf-hybrid --port 5002 --force-ocr | opendataloader-pdf --hybrid docling-fast file1.pdf file2.pdf folder/ | | Non-English scanned PDF | Hybrid + OCR | pip install "opendataloader-pdf[hybrid]" | opendataloader-pdf-hybrid --port 5002 --force-ocr --ocr-lang "ko,en" | opendataloader-pdf --hybrid docling-fast file1.pdf file2.pdf folder/ | | Mathematical formulas | Hybrid + formula | pip install "opendataloader-pdf[hybrid]" | opendataloader-pdf-hybrid --enrich-formula | opendataloader-pdf --hybrid docling-fast --hybrid-mode full file1.pdf file2.pdf folder/ | | Charts needing description | Hybrid + picture | pip install "opendataloader-pdf[hybrid]" | opendataloader-pdf-hybrid --enrich-picture-description | opendataloader-pdf --hybrid docling-fast --hybrid-mode full file1.pdf file2.pdf folder/ | | Untagged PDFs needing accessibility | Auto-tagging → Tagged PDF | pip install opendataloader-pdf | None needed | opendataloader-pdf --format tagged-pdf file1.pdf file2.pdf folder/ | ## Quick Start ### Python bash pip install -U opendataloader-pdf python import opendataloader_pdf # Batch all files in one call — each convert() spawns a JVM process, so repeated calls are slow opendataloader_pdf.convert( input_path=["file1.pdf", "file2.pdf", "folder/"], output_dir="output/", format="markdown,json" ) ### Node.js bash npm install @opendataloader/pdf typescript import { convert } from '@opendataloader/pdf'; await convert(['file1.pdf', 'file2.pdf', 'folder/'], { outputDir: 'output/', format: 'markdown,json' }); ### Java xml org.opendataloader opendataloader-pdf-core Python Quick Start (https://opendataloader.org/docs/quick-start-python) | Node.js Quick Start (https://opendataloader.org/docs/quick-start-nodejs) | Java Quick Start (https://opendataloader.org/docs/quick-start-java) ## Hybrid Mode: #1 Accuracy for Complex PDFs Hybrid mode combines fast local Java processing with AI backends. Simple pages stay local (0.02s); complex pages route to AI for +90% table accuracy. bash pip install -U "opendataloader-pdf[hybrid]" Terminal 1 — Start the backend server: bash opendataloader-pdf-hybrid --port 5002 Terminal 2 — Process PDFs: bash # Batch all files in one call — each invocation spawns a JVM process, so repeated calls are slow opendataloader-pdf --hybrid docling-fast file1.pdf file2.pdf folder/ Python: python # Batch all files in one call — each convert() spawns a JVM process, so repeated calls are slow opendataloader_pdf.convert( input_path=["file1.pdf", "file2.pdf", "folder/"], output_dir="output/", hybrid="docling-fast" ) ### OCR for Scanned PDFs Start the backend with --force-ocr for image-based PDFs with no selectable text: bash opendataloader-pdf-hybrid --port 5002 --force-ocr For non-English documents, specify the language: bash opendataloader-pdf-hybrid --port 5002 --force-ocr --ocr-lang "ko,en" Supported languages: en, ko, ja, ch_sim, ch_tra, de, fr, ar, and more. ### Formula Extraction (LaTeX) Extract mathematical formulas as LaTeX from scientific PDFs: bash # Server: enable formula enrichment opendataloader-pdf-hybrid --enrich-formula # Batch all files in one call — each invocation spawns a JVM process, so repeated calls are slow opendataloader-pdf --hybrid docling-fast --hybrid-mode full file1.pdf file2.pdf folder/ Output in JSON: json { "type": "formula", "page number": 1, "bounding box": [226.2, 144.7, 377.1, 168.7], "content": "\\frac{f(x+h) - f(x)}{h}" } > Note: Formula and picture description enrichments require --hybrid-mode full on the client side. ### Chart & Image Description Generate AI descriptions for charts and images — useful for RAG search and accessibility alt text: bash # Server opendataloader-pdf-hybrid --enrich-picture-description # Batch all files in one call — each invocation spawns a JVM process, so repeated calls are slow opendataloader-pdf --hybrid docling-fast --hybrid-mode full file1.pdf file2.pdf folder/ Output in JSON: json { "type": "picture", "page number": 1, "bounding box": [72.0, 400.0, 540.0, 650.0], "description": "A bar chart showing waste generation by region from 2016 to 2030..." } > Uses SmolVLM (256M), a lightweight vision model. Custom prompts supported via --picture-description-prompt. ### Hancom Data Loader Integration — Coming Soon Enterprise-grade AI document analysis via Hancom Data Loader (https://sdk.hancom.com/en/services/1?utm_source=github&utm_medium=readme&utm_campaign=opendataloader-pdf) — customer-customized models trained on your domain-specific documents. 30+ element types (tables, charts, formulas, captions, footnotes, etc.), VLM-based image/chart understanding, complex table extraction (merged cells, nested tables), SLA-backed OCR for scanned documents, and native HWP/HWPX support. Supports PDF, DOCX, XLSX, PPTX, HWP, PNG, JPG. Live demo (https://livedemo.sdk.hancom.com/en/dataloader?utm_source=github&utm_medium=readme&utm_campaign=opendataloader-pdf) Hybrid Mode Guide (https://opendataloader.org/docs/hybrid-mode) ## Output Formats | Format | Use Case | |––––|–––––| | JSON | Structured data with bounding boxes, semantic types | | Markdown | Clean text for LLM context, RAG chunks | | HTML | Web display with styling | | Annotated PDF | Visual debugging — see detected structures (sample (https://opendataloader.org/demo/samples/01030000000000)) | | Text | Plain text extraction | Combine formats: format="json,markdown" ### JSON Output Example json { "type": "heading", "id": 42, "level": "Title", "page number": 1, "bounding box": [72.0, 700.0, 540.0, 730.0], "heading level": 1, "font": "Helvetica-Bold", "font size": 24.0, "text color": "[0.0]", "content": "Introduction" } | Field | Description | |—––|———––| | type | Element type: heading, paragraph, table, list, image, caption, formula | | id | Unique identifier for cross-referencing | | page number | 1-indexed page reference | | bounding box | [left, bottom, right, top] in PDF points (72pt = 1 inch) | | heading level | Heading depth (1+) | | content | Extracted text | Full JSON Schema (https://opendataloader.org/docs/reference/json-schema) ## Advanced Features ### Tagged PDF Support When a PDF has structure tags, OpenDataLoader extracts the exact layout the author intended — no guessing, no heuristics. Headings, lists, tables, and reading order are preserved from the source. python # Batch all files in one call — each convert() spawns a JVM process, so repeated calls are slow opendataloader_pdf.convert( input_path=["file1.pdf", "file2.pdf", "folder/"], output_dir="output/", use_struct_tree=True # Use native PDF structure tags ) Most PDF parsers ignore structure tags entirely. Learn more (https://opendataloader.org/docs/tagged-pdf) ### AI Safety: Prompt Injection Protection PDFs can contain hidden prompt injection attacks. OpenDataLoader automatically filters: - Hidden text (transparent, zero-size fonts) - Off-page content - Suspicious invisible layers To sanitize sensitive data (emails, URLs, phone numbers → placeholders), enable it explicitly: bash # Batch all files in one call — each invocation spawns a JVM process, so repeated calls are slow opendataloader-pdf file1.pdf file2.pdf folder/ --sanitize AI Safety Guide (https://opendataloader.org/docs/ai-safety) ### LangChain Integration bash pip install -U langchain-opendataloader-pdf python from langchain_opendataloader_pdf import OpenDataLoaderPDFLoader loader = OpenDataLoaderPDFLoader( file_path=["file1.pdf", "file2.pdf", "folder/"], format="text" ) documents = loader.load() LangChain Docs (https://docs.langchain.com/oss/python/integrations/document_loaders/opendataloader_pdf) | GitHub (https://github.com/opendataloader-project/langchain-opendataloader-pdf) | PyPI (https://pypi.org/project/langcha