[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6GA67KBZ0MCMER209V1TMSS":3,"skill-same:01M6GA67KBZ0MCMER209V1TMSS":83},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":23,"publishedAt":43,"linkOnly":44,"markdownContent":45,"markdownBody":46,"frontMatter":47,"refName":61,"commitSha":62,"skillDirectory":63,"forksCount":64,"repositoryHtmlUrl":65,"repositoryHomepageUrl":6,"ownerHtmlUrl":66,"ownerBlogUrl":6,"ownerDescription":67,"updatedAt":68,"install":69,"resources":82},"01M6GA67KBZ0MCMER209V1TMSS","ocr-and-documents",null,"Extract text from PDFs\u002Fscans (pymupdf, marker-pdf).","zapabob\u002FSkills","zapabob","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F130830376?v=4","01M6DBMYZAGRTFQDSQYW3W764P","峯岸 亮","01M6DBMZ3GKDSJ01NZ9XTZD7JD",2,"MIT","https:\u002F\u002Fgithub.com\u002Fzapabob\u002FSkills\u002Fblob\u002Fmain\u002Farchive\u002Fskill-md-reviewed\u002Fsha256\u002F85\u002F856c1bce359ca7da0cc83f06e7b6b59f9445c16084d0510fc1541101af57f263\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000002","文档处理","SCENARIO","使用场景",[24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42],"ocr and documents","documents","ocr","marker","docx","text-extraction","pymupdf","arxiv","extract","hermes agent","hermes","text","extraction","powerpoint","windows","research","linux","macos","PDF","2026-11-10 10:12:30",false,"---\nname: ocr-and-documents\ndescription: \"Extract text from PDFs\u002Fscans (pymupdf, marker-pdf).\"\nversion: 2.3.0\nauthor: Hermes Agent\nlicense: MIT\nplatforms: [linux, macos, windows]\nmetadata:\n  hermes:\n    tags: [PDF, Documents, Research, Arxiv, Text-Extraction, OCR]\n    related_skills: [pdf, docx, powerpoint]\n---\n\n# PDF & Document Extraction\n\nFor DOCX: see the `docx` skill (create\u002Fedit) or use `python-docx` for structured reads.\nFor PPTX: see the `powerpoint` skill (full create\u002Fread\u002Fedit support).\nFor PDF manipulation (merge, split, forms, watermarks, creation): see the `pdf` skill.\nThis skill covers **text extraction from PDFs and scanned documents**.\n\n> **Coming from a `read_file` EXTRACTION COVERAGE WARNING?** `read_file` auto-converts local PDFs but reads the text layer only; the warning footer lists the pages that yielded no text (scanned images). For a handful of pages, render + vision is fastest: `pdftoppm -jpeg -r 150 -f N -l N file.pdf \u002Ftmp\u002Fpage` then `vision_analyze` each image. For bulk OCR of many pages, use marker-pdf below (Step 2).\n\n## Step 1: Remote URL Available?\n\nIf the document has a URL, **always try `web_extract` first**:\n\n```\nweb_extract(urls=[\"https:\u002F\u002Farxiv.org\u002Fpdf\u002F2402.03300\"])\nweb_extract(urls=[\"https:\u002F\u002Fexample.com\u002Freport.pdf\"])\n```\n\nThis handles PDF-to-markdown conversion via Firecrawl with no local dependencies.\n\nOnly use local extraction when: the file is local, web_extract fails, or you need batch processing.\n\n## Step 2: Choose Local Extractor\n\n| Feature | pymupdf (~25MB) | marker-pdf (~3-5GB) |\n|---------|-----------------|---------------------|\n| **Text-based PDF** | ✅ | ✅ |\n| **Scanned PDF (OCR)** | ❌ | ✅ (90+ languages) |\n| **Tables** | ✅ (basic) | ✅ (high accuracy) |\n| **Equations \u002F LaTeX** | ❌ | ✅ |\n| **Code blocks** | ❌ | ✅ |\n| **Forms** | ❌ | ✅ |\n| **Headers\u002Ffooters removal** | ❌ | ✅ |\n| **Reading order detection** | ❌ | ✅ |\n| **Images extraction** | ✅ (embedded) | ✅ (with context) |\n| **Images → text (OCR)** | ❌ | ✅ |\n| **EPUB** | ✅ | ✅ |\n| **Markdown output** | ✅ (via pymupdf4llm) | ✅ (native, higher quality) |\n| **Install size** | ~25MB | ~3-5GB (PyTorch + models) |\n| **Speed** | Instant | ~1-14s\u002Fpage (CPU), ~0.2s\u002Fpage (GPU) |\n\n**Decision**: Use pymupdf unless you need OCR, equations, forms, or complex layout analysis.\n\nIf the user needs marker capabilities but the system lacks ~5GB free disk:\n> \"This document needs OCR\u002Fadvanced extraction (marker-pdf), which requires ~5GB for PyTorch and models. Your system has [X]GB free. Options: free up space, provide a URL so I can use web_extract, or I can try pymupdf which works for text-based PDFs but not scanned documents or equations.\"\n\n---\n\n## pymupdf (lightweight)\n\n```bash\npip install pymupdf pymupdf4llm\n```\n\n**Via helper script**:\n```bash\npython scripts\u002Fextract_pymupdf.py document.pdf              # Plain text\npython scripts\u002Fextract_pymupdf.py document.pdf --markdown    # Markdown\npython scripts\u002Fextract_pymupdf.py document.pdf --tables      # Tables\npython scripts\u002Fextract_pymupdf.py document.pdf --images out\u002F # Extract images\npython scripts\u002Fextract_pymupdf.py document.pdf --metadata    # Title, author, pages\npython scripts\u002Fextract_pymupdf.py document.pdf --pages 0-4   # Specific pages\n```\n\n**Inline**:\n```bash\npython -c \"\nimport pymupdf\ndoc = pymupdf.open('document.pdf')\nfor page in doc:\n    print(page.get_text())\n\"\n```\n\n---\n\n## marker-pdf (high-quality OCR)\n\n```bash\n# Check disk space first\npython scripts\u002Fextract_marker.py --check\n\npip install marker-pdf\n```\n\n**Via helper script**:\n```bash\npython scripts\u002Fextract_marker.py document.pdf                # Markdown\npython scripts\u002Fextract_marker.py document.pdf --json         # JSON with metadata\npython scripts\u002Fextract_marker.py document.pdf --output_dir out\u002F  # Save images\npython scripts\u002Fextract_marker.py scanned.pdf                 # Scanned PDF (OCR)\npython scripts\u002Fextract_marker.py document.pdf --use_llm      # LLM-boosted accuracy\n```\n\n**CLI** (installed with marker-pdf):\n```bash\nmarker_single document.pdf --output_dir .\u002Foutput\nmarker \u002Fpath\u002Fto\u002Ffolder --workers 4    # Batch\n```\n\n---\n\n## Arxiv Papers\n\n```\n# Abstract only (fast)\nweb_extract(urls=[\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2402.03300\"])\n\n# Full paper\nweb_extract(urls=[\"https:\u002F\u002Farxiv.org\u002Fpdf\u002F2402.03300\"])\n\n# Search\nweb_search(query=\"arxiv GRPO reinforcement learning 2026\")\n```\n\n## Split, Merge & Search\n\npymupdf handles these natively — use `execute_code` or inline Python:\n\n```python\n# Split: extract pages 1-5 to a new PDF\nimport pymupdf\ndoc = pymupdf.open(\"report.pdf\")\nnew = pymupdf.open()\nfor i in range(5):\n    new.insert_pdf(doc, from_page=i, to_page=i)\nnew.save(\"pages_1-5.pdf\")\n```\n\n```python\n# Merge multiple PDFs\nimport pymupdf\nresult = pymupdf.open()\nfor path in [\"a.pdf\", \"b.pdf\", \"c.pdf\"]:\n    result.insert_pdf(pymupdf.open(path))\nresult.save(\"merged.pdf\")\n```\n\n```python\n# Search for text across all pages\nimport pymupdf\ndoc = pymupdf.open(\"report.pdf\")\nfor i, page in enumerate(doc):\n    results = page.search_for(\"revenue\")\n    if results:\n        print(f\"Page {i+1}: {len(results)} match(es)\")\n        print(page.get_text(\"text\"))\n```\n\nNo extra dependencies needed — pymupdf covers split, merge, search, and text extraction in one package.\n\n---\n\n## Notes\n\n- `web_extract` is always first choice for URLs\n- pymupdf is the safe default — instant, no models, works everywhere\n- marker-pdf is for OCR, scanned docs, equations, complex layouts — install only when needed\n- Both helper scripts accept `--help` for full usage\n- marker-pdf downloads ~2.5GB of models to `~\u002F.cache\u002Fhuggingface\u002F` on first use\n- For Word docs: `pip install python-docx` (better than OCR — parses actual structure)\n- For PowerPoint: see the `powerpoint` skill (uses python-pptx)\n","# PDF & Document Extraction\n\nFor DOCX: see the `docx` skill (create\u002Fedit) or use `python-docx` for structured reads.\nFor PPTX: see the `powerpoint` skill (full create\u002Fread\u002Fedit support).\nFor PDF manipulation (merge, split, forms, watermarks, creation): see the `pdf` skill.\nThis skill covers **text extraction from PDFs and scanned documents**.\n\n> **Coming from a `read_file` EXTRACTION COVERAGE WARNING?** `read_file` auto-converts local PDFs but reads the text layer only; the warning footer lists the pages that yielded no text (scanned images). For a handful of pages, render + vision is fastest: `pdftoppm -jpeg -r 150 -f N -l N file.pdf \u002Ftmp\u002Fpage` then `vision_analyze` each image. For bulk OCR of many pages, use marker-pdf below (Step 2).\n\n## Step 1: Remote URL Available?\n\nIf the document has a URL, **always try `web_extract` first**:\n\n```\nweb_extract(urls=[\"https:\u002F\u002Farxiv.org\u002Fpdf\u002F2402.03300\"])\nweb_extract(urls=[\"https:\u002F\u002Fexample.com\u002Freport.pdf\"])\n```\n\nThis handles PDF-to-markdown conversion via Firecrawl with no local dependencies.\n\nOnly use local extraction when: the file is local, web_extract fails, or you need batch processing.\n\n## Step 2: Choose Local Extractor\n\n| Feature | pymupdf (~25MB) | marker-pdf (~3-5GB) |\n|---------|-----------------|---------------------|\n| **Text-based PDF** | ✅ | ✅ |\n| **Scanned PDF (OCR)** | ❌ | ✅ (90+ languages) |\n| **Tables** | ✅ (basic) | ✅ (high accuracy) |\n| **Equations \u002F LaTeX** | ❌ | ✅ |\n| **Code blocks** | ❌ | ✅ |\n| **Forms** | ❌ | ✅ |\n| **Headers\u002Ffooters removal** | ❌ | ✅ |\n| **Reading order detection** | ❌ | ✅ |\n| **Images extraction** | ✅ (embedded) | ✅ (with context) |\n| **Images → text (OCR)** | ❌ | ✅ |\n| **EPUB** | ✅ | ✅ |\n| **Markdown output** | ✅ (via pymupdf4llm) | ✅ (native, higher quality) |\n| **Install size** | ~25MB | ~3-5GB (PyTorch + models) |\n| **Speed** | Instant | ~1-14s\u002Fpage (CPU), ~0.2s\u002Fpage (GPU) |\n\n**Decision**: Use pymupdf unless you need OCR, equations, forms, or complex layout analysis.\n\nIf the user needs marker capabilities but the system lacks ~5GB free disk:\n> \"This document needs OCR\u002Fadvanced extraction (marker-pdf), which requires ~5GB for PyTorch and models. Your system has [X]GB free. Options: free up space, provide a URL so I can use web_extract, or I can try pymupdf which works for text-based PDFs but not scanned documents or equations.\"\n\n---\n\n## pymupdf (lightweight)\n\n```bash\npip install pymupdf pymupdf4llm\n```\n\n**Via helper script**:\n```bash\npython scripts\u002Fextract_pymupdf.py document.pdf              # Plain text\npython scripts\u002Fextract_pymupdf.py document.pdf --markdown    # Markdown\npython scripts\u002Fextract_pymupdf.py document.pdf --tables      # Tables\npython scripts\u002Fextract_pymupdf.py document.pdf --images out\u002F # Extract images\npython scripts\u002Fextract_pymupdf.py document.pdf --metadata    # Title, author, pages\npython scripts\u002Fextract_pymupdf.py document.pdf --pages 0-4   # Specific pages\n```\n\n**Inline**:\n```bash\npython -c \"\nimport pymupdf\ndoc = pymupdf.open('document.pdf')\nfor page in doc:\n    print(page.get_text())\n\"\n```\n\n---\n\n## marker-pdf (high-quality OCR)\n\n```bash\n# Check disk space first\npython scripts\u002Fextract_marker.py --check\n\npip install marker-pdf\n```\n\n**Via helper script**:\n```bash\npython scripts\u002Fextract_marker.py document.pdf                # Markdown\npython scripts\u002Fextract_marker.py document.pdf --json         # JSON with metadata\npython scripts\u002Fextract_marker.py document.pdf --output_dir out\u002F  # Save images\npython scripts\u002Fextract_marker.py scanned.pdf                 # Scanned PDF (OCR)\npython scripts\u002Fextract_marker.py document.pdf --use_llm      # LLM-boosted accuracy\n```\n\n**CLI** (installed with marker-pdf):\n```bash\nmarker_single document.pdf --output_dir .\u002Foutput\nmarker \u002Fpath\u002Fto\u002Ffolder --workers 4    # Batch\n```\n\n---\n\n## Arxiv Papers\n\n```\n# Abstract only (fast)\nweb_extract(urls=[\"https:\u002F\u002Farxiv.org\u002Fabs\u002F2402.03300\"])\n\n# Full paper\nweb_extract(urls=[\"https:\u002F\u002Farxiv.org\u002Fpdf\u002F2402.03300\"])\n\n# Search\nweb_search(query=\"arxiv GRPO reinforcement learning 2026\")\n```\n\n## Split, Merge & Search\n\npymupdf handles these natively — use `execute_code` or inline Python:\n\n```python\n# Split: extract pages 1-5 to a new PDF\nimport pymupdf\ndoc = pymupdf.open(\"report.pdf\")\nnew = pymupdf.open()\nfor i in range(5):\n    new.insert_pdf(doc, from_page=i, to_page=i)\nnew.save(\"pages_1-5.pdf\")\n```\n\n```python\n# Merge multiple PDFs\nimport pymupdf\nresult = pymupdf.open()\nfor path in [\"a.pdf\", \"b.pdf\", \"c.pdf\"]:\n    result.insert_pdf(pymupdf.open(path))\nresult.save(\"merged.pdf\")\n```\n\n```python\n# Search for text across all pages\nimport pymupdf\ndoc = pymupdf.open(\"report.pdf\")\nfor i, page in enumerate(doc):\n    results = page.search_for(\"revenue\")\n    if results:\n        print(f\"Page {i+1}: {len(results)} match(es)\")\n        print(page.get_text(\"text\"))\n```\n\nNo extra dependencies needed — pymupdf covers split, merge, search, and text extraction in one package.\n\n---\n\n## Notes\n\n- 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