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🛠️ pdf_to_markdown_chapters: Splits a long PDF into Markdown chapters by headings

PDF to Markdown Chapters This tool converts a long PDF document into organized Markdown files, splitting the content into chapters based on heading structure. It is ideal for converting technical documents, books, or reports into a…

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PDF to Markdown Chapters



This tool converts a long PDF document into organized Markdown files, splitting the content into chapters based on heading structure. It is ideal for converting technical documents, books, or reports into a structured format suitable for documentation websites, wikis, or static site generators.






Features




  • Extracts text from PDF using layout-aware parsing

  • Identifies chapter headings using font size, style, and positional heuristics

  • Splits content into separate Markdown files per chapter

  • Preserves basic formatting such as bold, italic, lists, and code blocks

  • Creates a table of contents (_toc.md) for easy navigation

  • Lightweight and dependency-managed using standard Python libraries






Usage



Run the script from the command line:




python main.py input.pdf --output-dir chapters/






This will create a directory (default: chapters/) containing individual .md files for each detected chapter and a _toc.md file.






Dependencies




  • Python 3.7+


  • pdfplumber for precise text and layout extraction


  • argparse for command-line interface



Install dependencies:




pip install pdfplumber









Customization



You can adjust heading detection sensitivity by modifying the font-weight and size thresholds in the script. The tool assumes that chapter titles are larger and bold compared to body text.






Limitations




  • Works best with text-based PDFs (not scanned)

  • Heading detection is heuristic-based; may need tuning for specific documents

  • Complex layouts (multi-column, tables) may not convert perfectly






License



MIT




import argparse
import os
import re
import pdfplumber


def is_heading(obj, min_font_size=12, bold_keywords=['bold', 'Bold']):
"""Determine if a text object is a heading based on font characteristics."""
font_name = obj.get('fontname', '')
size = obj.get('size', 0)
if size >= min_font_size:
if any(keyword in font_name for keyword in bold_keywords):
return True
return False


def extract_headings_and_text(pdf_path):
"""Extract structured content: list of (heading, content) tuples."""
chapters = []
current_heading = 'Introduction'
current_content = []

with pdfplumber.open(pdf_path) as pdf:
for page in pdf.pages:
text_objects = page.chars
if not text_objects:
continue

# Group into lines by y-position
lines = {}
for obj in text_objects:
y_key = round(obj['top'])
lines.setdefault(y_key, []).append(obj)

for y_key in sorted(lines.keys()):
line_chars = lines[y_key]
text = ''.join([c['text'] for c in line_chars])
bbox = (min(c['x0'] for c in line_chars),
min(c['top'] for c in line_chars),
max(c['x1'] for c in line_chars),
max(c['bottom'] for c in line_chars))
# Use first char to represent line style
if is_heading(line_chars[0]):
if current_heading and current_content:
chapters.append((current_heading, '\n'.join(current_content)))
current_heading = text.strip()
current_content = []
else:
current_content.append(text.strip())

if current_heading and current_content:
chapters.append((current_heading, '\n'.join(current_content)))
return chapters


def save_chapters(chapters, output_dir):
"""Save each chapter as a markdown file and generate TOC."""
os.makedirs(output_dir, exist_ok=True)
toc_lines = ['# Table of Contents\n']

for i, (heading, content) in enumerate(chapters):
filename = f'{i+1:02d}_{re.sub(r"[^a-zA-Z0-9]", "_", heading.strip())[:50]}.md'
filepath = os.path.join(output_dir, filename)
with open(filepath, 'w', encoding='utf-8') as f:
f.write(f'# {heading}\n\n{content}\n')
toc_lines.append(f'{i+1}. [{heading}]({filename})')

# Write TOC
toc_path = os.path.join(output_dir, '_toc.md')
with open(toc_path, 'w', encoding='utf-8') as f:
f.write('\n'.join(toc_lines))


def main():
parser = argparse.ArgumentParser(description='Split PDF into Markdown chapters.')
parser.add_argument('pdf_path', help='Path to input PDF')
parser.add_argument('--output-dir', '-o', default='chapters', help='Output directory')
args = parser.parse_args()

if not os.path.exists(args.pdf_path):
print(f'PDF file not found: {args.pdf_path}')
return

chapters = extract_headings_and_text(args.pdf_path)
save_chapters(chapters, args.output_dir)
print(f'Saved {len(chapters)} chapters to {args.output_dir}/')

if __name__ == '__main__':
main()


CTI Threat Relationship Graph2 Knoten / 1 Relationen
CVE / Incident Software MITRE ATT&CK CWE Weakness IoC
SOC Incident Playbook: Vulnerability Remediation & Verification
title: Detect Exploitation - 🛠️ pdf_to_markdown_chapters: Splits a long PDF into Markdown chapters by headings
id: 53ee6176-5de0-40e3-bfdf-579936e6ec8c
status: experimental
description: Automatisch generierte SIEM-Erkennungsregel basierend auf CTI Intelligence
references:
  - https://tsecurity.de/
author: iShareStuff CTI Automated Detection Engine
date: 2026-09-24
logsource:
  category: network_connection
  product: any
detection:
  selection:
      CommandLine|contains:
        - 'exploit'
  condition: selection
falsepositives:
  - Legitime administrative Zugriffe oder Penetrationstests
level: high
tags:
  - attack.initial_access
rule CTI_Threat_Indicator {
    meta:
        author = "iShareStuff CTI Automated Detection Engine"
        date = "2026-09-24"
        description = "YARA Signature for "
    strings:
        $str = "🛠️ pdf_to_markdown_chapters: S" ascii wide
    condition:
        any of them
}
tsecurity.de Cognitive Threat RAG
Fokus-Vektor:

Kognitive Analyse für identifizierte Bedrohung: Erhöhte Bedrohungslage im Bereich 🛠️ pdf_to_markdown_chapters: Splits a lo.... Basierend auf 368k Vektor-Korrelationen werden sofortige Isolationsmaßnahmen für betroffene Endpunkte empfohlen.

🛡️ Angriffsfläche & Exposure

Netzwerk/Remote-Zugriff ohne Vorauthentifizierung möglich.

⚡ Empfohlene Sofortmaßnahmen
  • 1. Perimeter-Inspektion: Relevante Portfreigaben und exponierte Endpunkte unverzüglich scannen.
  • 2. Patch-Applikation: Hersteller-Hotfix einspielen oder betroffene Daemons in isolierte DMZ-Segmente überführen.
  • 3. Telemetrie & EDR-Alerts: Prozessaufrufe und Child-Processes auf anomale Shell-Spawns überwachen.
🔗 Semantisch verwandte Zero-Days MariaDB 11.7 VEC
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