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Parse scanned PDFs with tables into Markdown and JSON

Run PaddleOCR's PP-StructureV3 pipeline on scans to recover layout, tables, formulas and reading order as Markdown or JSON.

The problem

How it works

PP-StructureV3 combines layout detection, OCR and optional table, formula, chart and seal recognition, plus multi-column reading order recovery. You run it with `paddleocr pp_structurev3 -i <file>` or the Python `PPStructureV3().predict()` call, then save results as JSON, Markdown with extracted images, or Word; multi-page PDFs can be merged into one Markdown file.

Results and limitations

The README says PaddleOCR turns PDFs and images into structured, LLM-ready JSON or Markdown for RAG and agent use, with support for many languages.

Before you try it

PaddlePaddle must be installed separately, the default models are large and can be slow on CPU, and the docs recommend a GPU or lighter models. Results depend on your setup and were not independently reproduced by Top 200 Repos.

Tools and repositories

Tools
Python · PaddlePaddle · paddleocr CLI · NVIDIA GPU (recommended)
Prerequisites
No API key for local runs; a GPU is strongly recommended for the default models.

Try it yourself: Extract structure from scanned files

Fill the fields to adapt the prompt. Empty fields stay as visible [placeholders].

Preview
Use PaddleOCR PP-StructureV3 to parse the files in [Input folder], which are mostly [Document kind] in [Language]. Check whether PaddlePaddle and a GPU are available and show me the install commands before running them. Process one sample file first, save JSON and Markdown to [Output folder], and show me the Markdown so I can judge table and reading-order quality before you run the full batch. For PDFs, merge pages into one Markdown file per document. Report per file: pages processed, tables found, and any page that failed or produced little text.

Validate extracted numbers against the original scan before using them in finance or legal work.

Sources

  1. PaddlePaddle/PaddleOCR README (opens in a new tab)

    Project documentation · github.com · Undated · Source checked 9 Nov 2026, 22:23 UTC

    What this source supports
    • Converts PDFs and images into structured JSON or Markdown for AI use
    • PP-StructureV3 and PaddleOCR-VL document parsing
    • Multilingual OCR across many languages
  2. PP-StructureV3 Pipeline Usage Tutorial (opens in a new tab)

    Project documentation · paddleocr.ai · Undated · Source checked 9 Nov 2026, 22:24 UTC

    What this source supports
    • Layout, table, formula, chart and seal recognition with reading order recovery
    • CLI `paddleocr pp_structurev3` and Python `PPStructureV3().predict()`
    • Saves JSON, Markdown and Word output; can merge PDF pages
    • Default models are large; GPU preferred, CPU fallback
Report a problem

Reported by source

Convert PDFs and Office files to Markdown for an LLM

Run MarkItDown on PDFs, Word, PowerPoint and Excel files to get structure-preserving Markdown an LLM can read cheaply.

github.commicrosoft/markitdown
Planned, not done

Auto-tag receipts and bills in Paperless-ngx with paperless-ai

paperless-ai suggests titles, tags, correspondents and dates for documents in a Paperless-ngx archive, using a local Ollama model or a cloud LLM, with manual review and undo.

github.comollama/ollama
Reported by source

Extract invoice fields locally with a vision model on Ollama

Stephen Sebastian replaced a paid cloud OCR API with Gemma 4 E4B on Ollama to pull date, amount and description from contractor invoice photos on his laptop.

dev.toollama/ollama
Reported by source

Get a new OCR model running on unfamiliar GPU hardware with Claude Code in a sandbox

Simon Willison gave Claude Code full permissions inside a Docker container on an NVIDIA Spark and had it get DeepSeek-OCR working; it took four prompts and under 40 minutes, with one nudge from him.

simonwillison.net · 20 Oct 2025anthropics/claude-code, pytorch/pytorch, huggingface/transformers
Reported by source

Build a formula-based analysis workbook with Agent Mode in Excel

Agent Mode in Excel plans formulas, sheets and charts from a prompt, checks and fixes its own results, and reports its validation steps; Microsoft reports 57.2% accuracy on SpreadsheetBench in its own evaluation.

microsoft.com · 29 Sept 2025
Planned, not done

Build a formula-driven Excel model with the Claude xlsx skill

Install Anthropic's xlsx skill in Claude Code so the agent writes live formulas, recalculates the workbook with LibreOffice and checks it for formula errors.

github.comanthropics/skills, anthropics/claude-code