PDF LaTeX Converter¶

The PDF LaTeX Converter is ReViewPoint's flagship document processing plugin, designed to intelligently extract, analyze, and structure content from PDF documents using advanced parsing techniques and AI-assisted analysis.

Primary Goal¶

Transform unstructured PDF documents into well-organized, machine-readable formats that facilitate automated review workflows, content analysis, and citation processing. The plugin serves as the foundation for ReViewPoint's document understanding capabilities.

Overview¶

This plugin combines sophisticated PDF parsing with Large Language Model (LLM) integration to extract document structure, identify key components, and generate multiple output formats. It's specifically designed for academic papers and research documents, maintaining the integrity of complex formatting while making content accessible for further processing.

Input and Output Specifications¶

Input Requirements¶

Supported Input Formats:

PDF documents (all versions)
Multi-page academic papers
Research documents with complex formatting
Papers with footnotes, citations, and bibliographies

Input Requirements:

Readable PDF files (not image-only scans)
Documents with identifiable text content
Academic or research paper format

Output Generation¶

Primary Outputs:

Structured Text (structured_trimmed.txt)
Hierarchically organized content with LaTeX-style formatting
Preserved section numbering and clean, machine-readable format
HTML Document (structured_trimmed.html)
Web-ready formatted content with proper heading hierarchy
Validated citation links and navigation-friendly structure
Bibliography JSON (bibliography_entries.json)
Structured reference data with extractable citation information
Searchable bibliography entries in integration-ready format
Processing Artifacts
parsed.txt - Raw extracted text
heading_candidates.txt - Debug information for heading detection
structured_cite.txt - Content with converted citations
status.json - Real-time processing status

How It Works: Processing Pipeline¶

The plugin follows an intelligent 8-step processing pipeline:

Step 1: PDF Parsing and Text Extraction¶

Line-by-line parsing with font size and style detection
Font change recognition to preserve superscripts and formatting
Page break tracking for accurate document navigation
Character-level analysis for precise text extraction

Step 2: AI-Powered Structure Detection¶

Heading extraction using LLM analysis of document structure
Table of contents identification and hierarchical mapping
Section and subsection recognition with numbering preservation
Chapter boundary detection for academic documents

Step 3: Footnote Processing¶

Automatic footnote detection and marker identification
Cross-reference matching between in-text markers and footnotes
Footnote text extraction from page footers
Citation conversion from numeric markers to \cite{} format

Step 4: Bibliography Analysis¶

Bibliography section identification and page range detection
Reference entry extraction with structure preservation
Bibliography splitting for large reference sections
Metadata extraction from individual citations

Step 5: Structure Matching and Validation¶

Heading-to-text alignment using intelligent matching algorithms
Structure validation against extracted table of contents
Content organization into hierarchical sections
Quality assurance for extracted structure

Step 6: Content Trimming and Optimization¶

Main content isolation from auxiliary sections
Structure refinement to focus on core chapters
Noise reduction to remove processing artifacts
Content validation for completeness

Step 7: Format Conversion¶

HTML generation with proper heading hierarchy
LaTeX structure creation for academic formatting
Citation validation and cross-reference checking
Multi-format output generation

Step 8: Status Tracking and Monitoring¶

Real-time progress tracking via JSON status files
Error logging and debugging information
Processing timestamps for workflow coordination
External monitoring capabilities for integration

Installation and Configuration¶

Prerequisites¶

# Required Python packages
pdfminer.six>=20221105  # PDF parsing
openai                  # LLM integration
PyPDF2>=3.0.1          # PDF manipulation
validators>=0.20.0      # Data validation
isbnlib>=3.10.14       # ISBN processing

Basic Setup¶

Install dependencies:

pip install -r requirements.txt

Manual Mode (No API Key Required):

python -m src.pipeline --manual

API Mode with OpenAI:

python -m src.pipeline --api-key YOUR_OPENAI_KEY

Advanced Configuration¶

Create config.ini for detailed settings:

[openai]
api_key = YOUR_OPENAI_KEY
model = gpt-4                # LLM model selection
max_tokens = 500            # Response length limit
temperature = 0.3           # Response creativity
request_interval = 1.0      # Rate limiting (seconds)

[processing]
pages_for_analysis = 1-10   # Page range for LLM analysis
debug_logging = true        # Enable detailed logs
preserve_formatting = true  # Maintain original formatting

Usage Examples¶

Basic Document Processing¶

# Process all PDFs in input directory
python -m src.pipeline --manual

# Process with OpenAI integration
python -m src.pipeline --config config.ini

# Process specific file with custom output
python -m src.pipeline --input-dir ./papers --output-dir ./processed

Advanced Usage¶

# Debug mode with detailed logging
python -m src.pipeline --log-level DEBUG --log-file detailed.log

# Extract titles only (no full processing)
python -m src.pipeline --print-extract-titles research_paper.pdf

# Custom page range for analysis
python -m src.pipeline --analysis-pages 1-15 --config config.ini

Integration with ReViewPoint¶

from pdf_latex_converter import PDFProcessor

# Initialize processor
processor = PDFProcessor(config_path="config.ini")

# Process uploaded document
result = processor.process_document(
    pdf_path="uploaded_paper.pdf",
    output_dir="./processed",
    include_bibliography=True
)

# Access structured results
structured_content = result.structured_text
bibliography = result.bibliography_entries
html_output = result.html_document

Technical Architecture¶

Core Components¶

PDF Reader (pdf_reader.py)

PDFMiner integration for robust PDF parsing
LineInfo dataclass for structured text representation
Font analysis with size, bold, and italic detection
Character-level processing for precision extraction

LLM Client (llm_client.py)

OpenAI API integration with configurable models
Rate limiting and error handling
Manual mode support for testing without API costs
Response caching for development efficiency

Structure Tools (structure_tools.py)

HTML conversion utilities
LaTeX formatting preservation
Content trimming algorithms
Structure validation functions

Bibliography Processing (bibliography.py)

Bibliography detection using pattern matching
Page range identification for reference sections
PDF splitting for targeted analysis
Entry extraction and structuring

AI Integration¶

The plugin leverages Large Language Models for intelligent document analysis:

Extract Titles Prompt:

Analyzes document structure to identify headings
Returns hierarchical JSON with chapters, sections, and subsections
Supports both English and German document formats
Preserves original numbering and formatting

Footnote Detection:

Identifies footnote markers and corresponding text
Extracts precise footnote content from page footers
Maintains reference accuracy for citation processing

Bibliography Processing:

Extracts structured bibliography entries
Identifies reference formatting patterns
Supports multiple citation styles

Performance and Troubleshooting¶

Processing Performance¶

Small Documents (< 20 pages): 30-60 seconds
Medium Documents (20-50 pages): 1-3 minutes
Large Documents (> 50 pages): 3-10 minutes
API Rate Limiting: Configurable delays between LLM calls

Common Issues and Solutions¶

PDF Reading Errors¶

Symptoms: "Failed to read PDF" errors

Solutions:

Verify PDF is not corrupted or password-protected
Check file permissions and accessibility
Try re-saving PDF with different settings

LLM API Issues¶

Symptoms: API timeout or rate limit errors

Solutions:

Increase request_interval in configuration
Verify API key validity and quota
Use manual mode for testing without API costs

Poor Structure Detection¶

Symptoms: Missing headings or incorrect hierarchy

Solutions:

Enable debug logging to analyze detection process
Adjust page range for LLM analysis
Verify document has clear structural formatting

Debug and Monitoring¶

Monitor processing progress via status files:

{
  "status": "extracting_bibliography",
  "timestamp": "2025-07-23T14:30:15.123456",
  "current_step": 7,
  "total_steps": 8,
  "estimated_completion": "2025-07-23T14:32:00"
}

Enable comprehensive logging:

python -m src.pipeline --log-level DEBUG --log-file converter.log

Development and Support¶

Development Setup¶

# Clone repository
git clone https://github.com/Swabble/pdf_latex_converter.git
cd pdf_latex_converter

# Development environment
python -m venv venv
source venv/bin/activate  # or venv\Scripts\activate on Windows
pip install -r requirements.txt

# Run tests
python -m pytest tests/

Contributing Guidelines¶

Code Standards: Follow PEP 8 and type hints
Testing: Add tests for new functionality
Documentation: Update docs for API changes
Performance: Consider memory and speed impacts

Support Channels¶

Repository: pdf_latex_converter
Issues: GitHub issue tracker for bugs and features
Documentation: Repository wiki for detailed guides
Community: ReViewPoint discussions for general help

License and Roadmap¶

License¶

This plugin is open source under the MIT License.

Current Capabilities (v1.0)¶

Intelligent PDF parsing and structure extraction
AI-powered heading and section detection
Bibliography processing and citation conversion
Multiple output format generation

Planned Enhancements (v2.0)¶

Enhanced OCR Support for scanned documents
Multi-language Processing beyond German/English
Advanced Table Detection and extraction
Real-time Collaborative Processing capabilities
Cloud Processing Integration for scalability
Machine Learning Models for improved accuracy

Overview¶

This plugin bridges the gap between different document formats in academic publishing, allowing reviewers and authors to work with their preferred format while maintaining document integrity and formatting.

Installation¶

Prerequisites¶

Python 3.8 or higher
LaTeX distribution (TeX Live, MiKTeX, or MacTeX)
ReViewPoint platform installed and configured
PDF processing libraries

Setup¶

Ensure the plugin repository is cloned (use the "Setup Plugin Repositories" task)
Navigate to the plugin directory:

cd plugin_prototypes/pdf_latex_converter

Install dependencies:

pip install -r requirements.txt

Configure your LaTeX distribution path in the configuration file

Features¶

High-Quality PDF to LaTeX Conversion¶

Text Extraction: Accurate text extraction maintaining formatting
Structure Preservation: Maintains document hierarchy and sections
Font Handling: Preserves text styling and font information
Layout Detection: Recognizes and converts document layouts

LaTeX to PDF Compilation¶

Multiple Engines: Support for pdfLaTeX, XeLaTeX, and LuaLaTeX
Bibliography Processing: Automatic BibTeX/Biber integration
Index Generation: Support for makeindex and xindy
Error Handling: Comprehensive compilation error reporting

Mathematical Formula Preservation¶

Equation Recognition: Advanced mathematical content detection
LaTeX Math Mode: Converts to appropriate LaTeX math environments
Symbol Mapping: Accurate mathematical symbol conversion
Complex Expressions: Handles multi-line equations and matrices

Table and Figure Extraction¶

Table Detection: Identifies and converts tabular data
Figure Extraction: Preserves images and diagrams
Caption Handling: Maintains figure and table captions
Cross-references: Preserves internal document references

Bibliography Handling¶

Reference Extraction: Identifies and converts reference lists
BibTeX Generation: Creates structured bibliography files
Citation Mapping: Maintains citation-reference relationships
Format Standardization: Ensures consistent bibliography formatting

Configuration¶

Environment Variables¶

# LaTeX configuration
LATEX_COMPILER=pdflatex
LATEX_PATH=/usr/local/texlive/2023/bin
BIBTEX_PROCESSOR=biber

# PDF processing
PDF_DPI=300
OCR_ENABLED=true
OCR_LANGUAGE=eng

# Output settings
OUTPUT_ENCODING=utf-8
PRESERVE_COMMENTS=true

Plugin Settings¶

Conversion Quality: Configure precision vs. speed trade-offs
LaTeX Packages: Specify required LaTeX packages for compilation
OCR Settings: Enable/configure optical character recognition
Output Format: Choose LaTeX document class and styling

Usage¶

Basic PDF to LaTeX Conversion¶

from pdf_latex_converter import PDFConverter

converter = PDFConverter()
latex_content = converter.pdf_to_latex("input.pdf")

# Save to file
with open("output.tex", "w", encoding="utf-8") as f:
    f.write(latex_content)

Advanced Conversion with Options¶

converter = PDFConverter(
    preserve_formatting=True,
    extract_images=True,
    ocr_enabled=True
)

# Convert with custom settings
result = converter.convert(
    input_file="paper.pdf",
    output_dir="./converted",
    options={
        "document_class": "article",
        "packages": ["amsmath", "graphicx", "hyperref"],
        "bibliography_style": "ieee"
    }
)

LaTeX to PDF Compilation¶

from pdf_latex_converter import LaTeXCompiler

compiler = LaTeXCompiler()
pdf_result = compiler.compile_latex(
    tex_file="document.tex",
    engine="pdflatex",
    runs=2  # For bibliography and cross-references
)

if pdf_result.success:
    print(f"PDF generated: {pdf_result.output_file}")
else:
    print(f"Compilation failed: {pdf_result.errors}")

Integration with ReViewPoint¶

The plugin integrates with ReViewPoint's workflow:

Upload Processing: Automatically converts uploaded PDFs
Review Mode: Allows reviewers to switch between PDF and LaTeX views
Annotation Sync: Synchronizes comments between formats
Export Options: Provides multiple output formats for authors

API Reference¶

PDFConverter Class¶

Methods¶

pdf_to_latex(pdf_path, options=None): Convert PDF to LaTeX
extract_images(pdf_path, output_dir): Extract images from PDF
extract_bibliography(pdf_path): Extract reference list
analyze_structure(pdf_path): Analyze document structure

LaTeXCompiler Class¶

Compilation Methods¶

compile_latex(tex_file, engine="pdflatex"): Compile LaTeX to PDF
validate_syntax(tex_file): Check LaTeX syntax
extract_dependencies(tex_file): List required packages
generate_bibliography(bib_file): Process bibliography

Supported Formats¶

Input Formats¶

PDF (all versions)
LaTeX (.tex files)
BibTeX (.bib files)
Plain text with formatting hints

Output Formats¶

LaTeX (.tex)
PDF (via LaTeX compilation)
HTML (experimental)
Markdown (basic conversion)

Troubleshooting¶

Common Issues¶

LaTeX Compilation Errors¶

Verify LaTeX distribution is properly installed
Check required packages are available
Review compilation logs for specific errors
Ensure file paths don't contain special characters

Poor PDF Conversion Quality¶

Increase PDF resolution settings
Enable OCR for scanned documents
Check source PDF quality and format
Adjust conversion parameters

Missing Mathematical Content¶

Enable mathematical formula detection
Check PDF contains actual text (not just images)
Verify LaTeX math packages are included
Review conversion settings for math handling

Performance Optimization¶

Large Documents: Process in sections for better performance
Memory Usage: Adjust processing chunk sizes
Quality vs Speed: Configure conversion precision levels
Parallel Processing: Enable multi-threading for batch operations

Development¶

Building from Source¶

# Clone repository
git clone https://github.com/Swabble/pdf_latex_converter.git
cd pdf_latex_converter

# Install development dependencies
pip install -r requirements-dev.txt

# Run tests
python -m pytest tests/

# Build documentation
sphinx-build docs/ docs/_build/

Contributing¶

We welcome contributions to improve the PDF LaTeX Converter:

Fork the repository
Create a feature branch
Implement your changes
Add comprehensive tests
Update documentation
Submit a pull request

Testing¶

The plugin includes comprehensive tests for:

PDF parsing accuracy
LaTeX generation quality
Mathematical content preservation
Bibliography extraction
Error handling scenarios

License¶

This plugin is open source and available under the MIT License. See the repository for full license details.

Support¶

Repository: pdf_latex_converter
Issues: Report bugs and feature requests on GitHub
Documentation: Comprehensive guides in the repository wiki

Roadmap¶

Current Version Features¶

Basic PDF to LaTeX conversion
LaTeX compilation support
Mathematical formula extraction
Image and table handling

Planned Enhancements¶

Improved OCR accuracy
Advanced table detection
Real-time collaborative editing
Cloud processing support
Machine learning-enhanced conversion