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AnandaAnugrahHandyanto
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ocr-and-documents

Extract text and data from PDFs and scanned documents using Python OCR tools.

Updated Jun 19, 2026
Search Tags:#ocr#pdf#markdown#pymupdf#text-extraction#document-extraction#marker-pdf
One-click install
npx skills add https://github.com/AnandaAnugrahHandyanto/savarez_agent --skill ocr-and-documents-anandaanugrahhandyanto
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ocr-and-documents
Source: https://github.com/AnandaAnugrahHandyanto/savarez_agent/tree/main/skills/productivity/ocr-and-documents
Command: npx skills add https://github.com/AnandaAnugrahHandyanto/savarez_agent --skill ocr-and-documents-anandaanugrahhandyanto

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pymupdf, pymupdf4llm, marker-pdf, and includes scripts (resource) components.

What problem does it solve?

Extract text and data from PDFs and scanned documents is time-consuming when done manually. This Skill combines lightweight text extraction with OCR-powered parsing to convert content into searchable, editable outputs.

Core Features & Use Cases

  • Text extraction from standard PDFs and images using pymupdf for fast, dependency-free workflows.
  • OCR-enabled extraction for scanned documents via marker-pdf to recover text, tables, code blocks, and layout.
  • Output formats include Markdown and structured data; useful for archiving, research, and content processing.
  • Use Case: A researcher downloads dozens of research PDFs and needs to pull out abstracts, tables, and figures for meta-analysis.

Quick Start

Run the provided scripts on a PDF to extract plain text with pymupdf or Markdown with pymupdf4llm.

Frequently Asked Questions about ocr-and-documents

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I extract text and tables from scanned PDFs for research analysis?▼

To extract text and tables from scanned PDFs, apply OCR-enabled parsing via marker-pdf to recover content, code blocks, and layout, outputting structured Markdown or JSON for research meta-analysis. This handles archival materials where standard text extraction fails.

What is the best way to convert standard PDFs to Markdown without losing layout?▼

Converting standard PDFs to Markdown is best handled by pymupdf4llm, which extracts text while preserving document layout. This yields dependency-free, fast output suitable for archiving and content processing workflows.

Does marker-pdf support extracting figures and images alongside text extraction?▼

Yes, marker-pdf supports extracting figures and images alongside text extraction. It recovers tables, code blocks, and layout from scanned documents, providing optional image and table extraction for comprehensive document parsing.

Can I use pymupdf for fast text extraction from large batches of research papers?▼

Yes, you can use pymupdf for fast text extraction from large batches of research papers. It provides lightweight, dependency-free parsing to quickly pull abstracts and text from standard PDFs without OCR overhead.

When should I not use standard PDF text extraction and switch to OCR instead?▼

You should switch to OCR when processing scanned documents or images where standard text extraction fails. If your PDFs are image-based archival materials without embedded text layers, marker-pdf is required to recover the content.

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