lazy-splitter 0.2.1

A collection of intelligent file splitting tools - PDF chapters, videos, audio, and more

Lazy Splitter

A collection of intelligent file splitting tools for the lazy developer. Split PDFs, EPUBs, and more with smart chapter detection.

🚀 Current Tools

📄 PDF Splitter

Intelligently detects chapters in PDF files and splits them into separate PDF files.

📚 EPUB Splitter

Intelligently detects chapters in EPUB files and splits them into separate EPUB files.

Features

PDF Splitter

• 🔍 Smart Chapter Detection: Automatically detects chapters using PDF bookmarks/TOC or text analysis
• 📑 Multiple Detection Strategies:
  • Bookmark/TOC extraction (fastest and most reliable)
  • Heuristic text analysis (font size, heading patterns, "Chapter N" detection)
  • Hybrid approach (combines both methods)
• 📊 Preview Mode: See detected chapters before splitting
• 🎯 Flexible Output: Customizable output directory and filename patterns
• 🚀 Progress Tracking: Rich progress bars for large files
• ⚙️ Configurable: Fine-tune detection sensitivity and patterns

EPUB Splitter

• 🔍 Smart Chapter Detection: Automatically detects chapters using native TOC, HTML structure, or manifest
• 📑 Multiple Detection Strategies:
  • Native TOC extraction (EPUB 2 NCX and EPUB 3 navigation)
  • Structural analysis (HTML heading tags)
  • Manifest-based splitting (spine items)
  • Hybrid approach (combines all methods)
• 📊 Preview Mode: See detected chapters before splitting
• 🎯 Flexible Output: Customizable output directory and filename patterns
• 📦 Resource Handling: Automatically copies referenced images, CSS, and fonts
• ⚙️ Configurable: Fine-tune detection sensitivity and TOC levels

Installation

From PyPI (recommended)

pip install lazy-splitter

From Source

git clone https://github.com/shankarpandala/lazy-splitter.git
cd lazy-splitter
pip install -e .

Usage

PDF Splitter

Split a PDF by chapters

pdf-splitter split input.pdf

Preview detected chapters without splitting

pdf-splitter preview input.pdf

Specify output directory

pdf-splitter split input.pdf -o output_dir

Choose detection strategy

# Use bookmarks only (fastest)
pdf-splitter split input.pdf --strategy bookmarks

# Use text analysis only (when bookmarks are missing)
pdf-splitter split input.pdf --strategy heuristic

# Use both methods (default)
pdf-splitter split input.pdf --strategy hybrid

Customize output filename pattern

pdf-splitter split input.pdf --pattern "{index:02d}_{title}.pdf"

EPUB Splitter

Split an EPUB by chapters

epub-splitter split ebook.epub

Preview detected chapters without splitting

epub-splitter preview ebook.epub

Specify output directory

epub-splitter split ebook.epub -o output_dir

Choose detection strategy

# Use native TOC only (fastest and most reliable)
epub-splitter split ebook.epub --strategy native

# Use HTML structure analysis (when TOC is missing)
epub-splitter split ebook.epub --strategy structural

# Use manifest-based splitting (one chapter per file)
epub-splitter split ebook.epub --strategy manifest

# Use hybrid approach (default)
epub-splitter split ebook.epub --strategy hybrid

Customize output filename pattern

epub-splitter split ebook.epub --pattern "{index:02d}_{title}.epub"

Examples

PDF Examples

# Basic usage
pdf-splitter split textbook.pdf

# Preview chapters first
pdf-splitter preview textbook.pdf

# Custom output location
pdf-splitter split textbook.pdf -o chapters/

# Force heuristic detection (for PDFs without bookmarks)
pdf-splitter split textbook.pdf --strategy heuristic --sensitivity high

EPUB Examples

# Basic usage
epub-splitter split novel.epub

# Preview chapters first
epub-splitter preview novel.epub

# Custom output location
epub-splitter split novel.epub -o chapters/

# Use structural detection (for EPUBs without TOC)
epub-splitter split novel.epub --strategy structural --sensitivity high

# Split by TOC level 2 (chapters instead of parts)
epub-splitter split textbook.epub --toc-level 2

How It Works

PDF Splitter

1. Bookmark/TOC Extraction: First tries to extract chapter information from PDF bookmarks or table of contents
2. Text Analysis Fallback: If bookmarks are unavailable, analyzes text for:
   • Font size changes (larger fonts often indicate headings)
   • Common chapter patterns ("Chapter 1", "CHAPTER ONE", etc.)
   • Page breaks combined with heading-like text
3. Smart Splitting: Creates individual PDF files for each detected chapter with preserved formatting and metadata

EPUB Splitter

1. Native TOC Extraction: First tries to extract chapter information from EPUB navigation (nav.xhtml or toc.ncx)
2. Structural Analysis Fallback: If TOC is unavailable, analyzes HTML structure for:
   • Heading tags (h1, h2, h3) based on sensitivity level
   • Semantic HTML structure
   • Title extraction from content
3. Manifest-based Fallback: Uses EPUB spine/manifest to create one chapter per content file
4. Smart Splitting: Creates individual EPUB files for each detected chapter with:
   • Preserved metadata and styling
   • Automatically copied resources (images, CSS, fonts)
   • Valid EPUB structure with regenerated manifest and spine

Requirements

• Python 3.8+
• PyMuPDF (for PDF manipulation)
• EbookLib (for EPUB manipulation)
• lxml (for HTML/XML parsing)
• Click (for CLI interface)
• Rich (for beautiful terminal output)

Development

# Install with development dependencies
pip install -e ".[dev]"

# Run tests
pytest

# Format code
black src/

# Type checking
mypy src/

License

MIT License - see LICENSE file for details

🗺️ Roadmap

✅ Completed

• ✅ PDF Splitter - Split PDFs by chapters with smart detection
• ✅ EPUB Splitter - Split EPUBs by chapters with TOC and structural analysis

Coming Soon

• 🎬 Video Splitter - Split videos by scenes, chapters, or silence detection
• 🎵 Audio Splitter - Split audio files by silence, chapters, or time intervals
• 📊 Document Splitter - Split Word docs, presentations, and more
• 🖼️ Image Splitter - Split image collections and multi-page TIFFs

Contributing

Contributions are welcome! We're building a suite of intelligent splitting tools. Please feel free to submit a Pull Request.

See CONTRIBUTING.md for guidelines.