PDF Extractor Skill#

Extract text and mathematical formulas from academic PDF papers. Supports both English and Chinese content.

When to Use This Skill#

Use this skill when:

• User needs to extract text and LaTeX formulas from PDF papers
• User mentions "PDF转文本", "PDF提取公式", "论文OCR"
• User wants to convert academic papers to Markdown format

Tool Selection#

ToolBest ForLanguagesMath QualityMarker (推荐)中英文论文、复杂公式Chinese + EnglishExcellentNougat纯英文论文、arXivEnglish onlyExcellent

推荐使用 Marker：支持中英文混排，公式识别效果更好。

---

Environment Setup#

Conda Environment: pdf-extractor Python Path: D:\anaconda3\envs\pdf-extractor\python.exe

Key Dependencies#

• PyTorch 2.10.0+cu128 (CUDA 12.8)
• marker-pdf (Surya OCR + Texify)
• nougat-ocr 0.1.17
• transformers

Important: Keep This Skill Self-Contained (No Extra Installs)#

This skill is expected to run using ONLY the existing pdf-extractor conda environment and the scripts in scripts/.

Rules:

• Do NOT run pip install ... / conda install ... / download random libraries during extraction.
• If a dependency is missing (e.g., Nougat crashes due to missing torchvision), do NOT try to fix by installing packages. Switch tools (prefer Marker) or report the environment issue.
• Slow runtime is normal for Marker (especially with --ark-code-latest). Prefer splitting the PDF rather than changing tools or adding dependencies.

Recommended approach for long PDFs:

• Use --page-range (0-based) to extract per page or small page batches.
• Merge the resulting markdown files afterward (simple concatenation is fine). Keep the combined file in the same folder as the per-page outputs so image links remain valid.

Example (per-page extraction with LLM mode):

bash

D:\anaconda3\envs\pdf-extractor\python.exe C:\Users\cr\.config\opencode\skills\pdf-extractor\scripts\pdf2md_marker.py "paper.pdf" --ark-code-latest --page-range "0" -o "out/page_01.md"

---

Tool 1: Marker (推荐 - 中英文支持)#

Command Line#

bash

# 转换中文论文 (默认支持中英文)
D:\anaconda3\envs\pdf-extractor\python.exe C:\Users\cr\.config\opencode\skills\pdf-extractor\scripts\pdf2md_marker.py "论文.pdf"

# 指定输出路径
D:\anaconda3\envs\pdf-extractor\python.exe C:\Users\cr\.config\opencode\skills\pdf-extractor\scripts\pdf2md_marker.py "paper.pdf" -o "output.md"

# 强制 OCR (用于扫描版 PDF)
D:\anaconda3\envs\pdf-extractor\python.exe C:\Users\cr\.config\opencode\skills\pdf-extractor\scripts\pdf2md_marker.py "scanned.pdf" --force-ocr

# 使用火山方舟 Coding Plan (OpenAI-compatible) 增强转换质量（表格/公式/跨页结构更稳）
# 注意：默认走 ark-code-latest，后台会自动路由到合适的模型
D:\anaconda3\envs\pdf-extractor\python.exe C:\Users\cr\.config\opencode\skills\pdf-extractor\scripts\pdf2md_marker.py "paper.pdf" --ark-code-latest

# 只跑第 1 页做快速验证（0-based page index）
D:\anaconda3\envs\pdf-extractor\python.exe C:\Users\cr\.config\opencode\skills\pdf-extractor\scripts\pdf2md_marker.py "paper.pdf" --ark-code-latest --page-range "0" -o "out_first_page.md"

# 如需自定义（不推荐）：也可以手动指定 --openai-base-url/--openai-api-key/--openai-model

# 指定语言
D:\anaconda3\envs\pdf-extractor\python.exe C:\Users\cr\.config\opencode\skills\pdf-extractor\scripts\pdf2md_marker.py "paper.pdf" --languages Chinese English Japanese

Python API#

python

import sys
sys.path.insert(0, r'C:\Users\cr\.config\opencode\skills\pdf-extractor\scripts')
from pdf2md_marker import convert_pdf, convert_pdf_cli

# 简单用法
output_file = convert_pdf_cli('论文.pdf', 'output.md')

# 完整 API
markdown_text, metadata = convert_pdf(
    'paper.pdf',
    output_dir='./output',
    force_ocr=False,
    batch_multiplier=2,
    languages=['Chinese', 'English']
)
print(markdown_text)

Marker Options#

OptionDescription-o, --outputOutput file (.md) or directory--force-ocrForce OCR even for text PDFs--batch-multiplierBatch size multiplier (default: 2)--languagesLanguages in document (default: Chinese English)

---

Tool 2: Nougat (纯英文论文)#

Command Line#

bash

# Convert entire PDF
D:\anaconda3\envs\pdf-extractor\python.exe C:\Users\cr\.config\opencode\skills\pdf-extractor\scripts\pdf2latex.py "paper.pdf"

# Convert specific pages
D:\anaconda3\envs\pdf-extractor\python.exe C:\Users\cr\.config\opencode\skills\pdf-extractor\scripts\pdf2latex.py "paper.pdf" -p 0-5

# Custom output
D:\anaconda3\envs\pdf-extractor\python.exe C:\Users\cr\.config\opencode\skills\pdf-extractor\scripts\pdf2latex.py "paper.pdf" -o output.mmd

# Save each page separately
D:\anaconda3\envs\pdf-extractor\python.exe C:\Users\cr\.config\opencode\skills\pdf-extractor\scripts\pdf2latex.py "paper.pdf" --per-page

Python API#

python

import sys
sys.path.insert(0, r'C:\Users\cr\.config\opencode\skills\pdf-extractor\scripts')
from pdf2latex import load_model, process_pdf, save_results

# Load model (uses GPU if available)
model, device = load_model()

# Process PDF
results = process_pdf('paper.pdf', model, device)

# Save as single markdown file
save_results(results, 'output.mmd')

# Or save per page
save_results(results, 'output_pages/', format='pages')

Nougat Options#

OptionDescription-o, --outputOutput file or directory-p, --pagesPage range (e.g., "0-5" or "1,3,5")-m, --modelModel tag (default: 0.1.0-base)--dpiRender DPI (default: 300)--cpuForce CPU mode--per-pageSave each page separately

---

Output Format#

Both tools output Markdown with LaTeX math:

• Text is extracted as regular markdown
• Mathematical formulas are in LaTeX format:
  • Inline: $formula$
  • Display: $$formula$$
• Tables, figures, and references are preserved
• Marker also extracts images to separate folder

---

Comparison#

FeatureMarkerNougatChinese Support✓ Excellent✗ PoorEnglish Support✓ Excellent✓ ExcellentMath Formulas✓ (Texify)✓ (Native)Table Extraction✓✓Image Extraction✓✗Speed (RTX 4060)2 min/page10-15 sec/pageOCR QualityExcellentGood

---

Troubleshooting#

Import Errors#

Make sure you're using the correct Python:

bash

D:\anaconda3\envs\pdf-extractor\python.exe your_script.py

CUDA Out of Memory#

Try CPU mode (Nougat) or reduce batch size (Marker):

bash

# Nougat: use CPU
D:\anaconda3\envs\pdf-extractor\python.exe pdf2latex.py paper.pdf --cpu

# Marker: reduce batch multiplier
D:\anaconda3\envs\pdf-extractor\python.exe pdf2md_marker.py paper.pdf --batch-multiplier 1

Chinese Characters Not Recognized#

Use Marker instead of Nougat for Chinese documents.

Slow Processing#

• Marker is slower but more accurate (uses multiple ML models)
• For faster processing on English-only papers, use Nougat
• Ensure GPU is being used (check CUDA availability)

---

Model Information#

Marker Models (downloaded automatically):

• Surya OCR: Text detection and recognition
• Texify: Math formula recognition
• Layout analysis models

Nougat Base Model (1.31 GB):

• Location: C:\Users\cr\.cache\torch\hub\nougat-0.1.0-base
• Best for: Standard academic papers, arXiv papers

---

Example Workflow#

python

import sys
sys.path.insert(0, r'C:\Users\cr\.config\opencode\skills\pdf-extractor\scripts')

def extract_paper(pdf_path, is_chinese=True):
    """
    Extract text and formulas from academic paper.

    Args:
        pdf_path: Path to PDF file
        is_chinese: True for Chinese papers, False for English only

    Returns:
        Extracted markdown text
    """
    if is_chinese:
        from pdf2md_marker import convert_pdf
        text, _ = convert_pdf(pdf_path, languages=['Chinese', 'English'])
    else:
        from pdf2latex import load_model, process_pdf
        model, device = load_model()
        results = process_pdf(pdf_path, model, device)
        text = '\n\n'.join([t for _, t in results])

    return text

# Usage
text = extract_paper('中文论文.pdf', is_chinese=True)
print(text)