PDF to DOCX Converter

A Python-based tool for converting PDF files to DOCX format with enhanced accuracy and customizable conversion parameters. This project is optimized for AWS SageMaker environments.

Overview

This converter uses the pdf2docx library with fine-tuned parameters to provide high-quality PDF to DOCX conversion. It automatically processes all PDF files in the current directory and generates corresponding DOCX files with improved layout detection, image handling, and text formatting.

Features

• Batch Processing: Converts all PDF files in the current directory
• Multi-processing Support: Utilizes multiple CPU cores for faster conversion
• Enhanced Accuracy: Fine-tuned parameters for better layout and formatting preservation
• Image Handling: Optimized image resolution and size controls
• Error Handling: Robust error handling with detailed feedback
• AWS SageMaker Ready: Pre-configured for SageMaker environments

Prerequisites

• AWS SageMaker environment
• Conda package manager
• Python 3.10+
• Access to private GitHub repository

Project Structure

pdf-to-docx-converter/
├── environment.yml          # Conda environment configuration
├── requirements.txt         # Python dependencies
├── main.py                 # Main conversion script
└── README.md              # This file

Installation & Setup

Step 1: Clone the Repository

In your SageMaker terminal, clone this private repository:

git clone https://github.com/your-username/your-private-repo.git
cd your-private-repo

Note: Ensure you have proper authentication set up for accessing private GitHub repositories in SageMaker. You may need to configure SSH keys or personal access tokens.

Step 2: Create Conda Environment

Create the conda environment using the provided configuration:

conda env create -f environment.yml

This will create an environment named myenv with all required dependencies.

Step 3: Initialize Conda

Initialize conda for your bash shell (required for SageMaker):

conda init bash

Step 4: Reload Shell Configuration

Reload your bash configuration to apply conda initialization:

source ~/.bashrc

Step 5: Activate Environment

Activate the newly created environment:

conda activate myenv

Your prompt should now show (myenv) indicating the environment is active.

Usage

Basic Usage

1. Place your PDF files in the same directory as main.py
2. Run the conversion script:

python main.py

Expected Output

The script will:

• Automatically detect all PDF files in the current directory
• Convert each PDF to DOCX format
• Save output files with the prefix convert_ (e.g., document.pdf → convert_document.docx)
• Display progress and status messages

Example Output

Converting: document1.pdf
Converted: convert_document1.docx
Converting: report.pdf
Converted: convert_report.docx

Configuration

The converter uses optimized parameters for high-quality conversion:

• Multi-processing: Enabled for faster processing
• Border Detection: Fine-tuned tolerance and clearance settings
• Image Processing: Optimized resolution and gap handling
• Text Alignment: Enhanced threshold settings for better layout preservation
• Margin Handling: Balanced top and bottom margin factors

Dependencies

Core Dependencies

• pdf2docx>=0.5.6 - Main conversion library
• PyMuPDF>=1.19.0 - PDF processing backend
• python-docx>=0.8.10 - DOCX file handling
• lxml>=4.6.0 - XML processing
• fonttools>=4.25.0 - Font handling

Environment Management

• Python 3.10
• Conda package manager

Troubleshooting

Common Issues

1. Conda activation error:

CondaError: Run 'conda init' before 'conda activate'

Solution: Run conda init bash followed by source ~/.bashrc

2. No PDF files found:

No PDF files found

Solution: Ensure PDF files are in the same directory as main.py

3. Permission errors: Solution: Check file permissions and ensure write access to the directory

4. Memory issues with large PDFs: Solution: Process files individually or increase SageMaker instance memory

GitHub Authentication in SageMaker

If you encounter authentication issues when cloning private repositories:

Option 1: Personal Access Token

git clone https://username:token@github.com/username/repo.git

Option 2: SSH Key Setup

ssh-keygen -t rsa -b 4096 -C "your_email@example.com"
# Add the public key to your GitHub account

Performance Optimization

For better performance in SageMaker:

1. Use appropriate instance types: Choose instances with sufficient CPU cores for multi-processing
2. Batch processing: Process multiple files in sequence rather than parallel for memory efficiency
3. Monitor resources: Use SageMaker's monitoring tools to track CPU and memory usage

Support

For issues related to:

• PDF conversion quality: Adjust parameters in main.py
• Performance: Consider instance type and file size optimization
• Dependencies: Check environment.yml and requirements.txt

Note: This project is designed specifically for AWS SageMaker environments. For local development, you may need to adjust the setup steps accordingly.