pdf2txt-multipage-extractor

pdf2txt-multipage-extractor is a Python tool designed to extract text from PDF files, processing entire directories efficiently. Optimized with multiprocessing, it can handle thousands of PDFs, making it suitable for large-scale document conversion tasks.

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Features

• Batch Processing: Converts all PDFs in a specified folder to text files.
• Multiprocessing: Utilizes multiple CPU cores for faster processing.
• Encoding Handling: Manages various text encodings to ensure accurate text representation.
• Indentation Preservation: Retains the original indentation and structure from PDFs.

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Repository Structure

• Pdf_test/: Directory containing sample PDFs for testing purposes.
• Python2/: Contains the Python 2 version of the extraction script.
• Python3/: Contains the Python 3 version of the extraction script.
• README.md: This file, providing an overview of the project.

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Getting Started

Prerequisites

• Python 3.x (recommended)
• pdfminer.six: A PDF parsing library for text extraction.

Installation

1. Clone the Repository:

   git clone https://git.1-hub.cnLorysHamadache/pdf2txt-multipage-extractor.git
   cd pdf2txt-multipage-extractor/Python3

2. Install Dependencies:

   pip install -r requirements.txt

Usage

1. Prepare Your Directories:

   • Place all target PDF files into a source directory (e.g., input_pdfs/).
   • Ensure an output directory exists for the text files (e.g., output_txts/).

2. Run the Extraction Script:

   python pdf_to_txt.py input_pdfs/ output_txts/

   Replace pdf_to_txt.py with the actual script name if different.

3. Review Output:

   • Each PDF in input_pdfs/ will have a corresponding .txt file in output_txts/ containing the extracted text.

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Implementation Details

1. Library Selection:

   • pdfminer.six was chosen for its reliability in text extraction across diverse PDF formats.

2. Extraction Process:

   • Single PDF Handling: Developed a function to extract text, ensuring proper encoding and indentation.
   • Batch Processing: Implemented a loop to process all PDFs in the source directory.
   • Multiprocessing: Utilized Python's multiprocessing module to enhance processing speed by leveraging multiple CPU cores.

3. Performance:

   • Tested on 10,031 PDFs, completing extraction in approximately 12 minutes under typical system load.
   • Optimization Attempts: Explored multithreading; however, observed no significant performance gains due to the Global Interpreter Lock (GIL) in Python.

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License

This project is licensed under the MIT License. See the LICENSE file for details.

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Contributing

Contributions are welcome! Please fork the repository and submit a pull request with your enhancements.

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Contact

For questions or suggestions, please open an issue in the repository.

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Note: This project is provided "as-is" without warranty of any kind. Use at your own discretion.