Flexible bulk PDF text extraction tool combining multiple processing engines, parallel processing, consolidated outputs and detailed logging into one workflow.

PDF-To-TXT is a powerful and flexible Python tool for extracting text from PDF files in bulk. It supports both the PyPDF and pdfminer.six engines for text extraction, with automatic fallback to OCR using Tesseract when standard extraction fails.

The program scans a specified input directory for PDF files and can process documents in parallel across multiple CPU cores. Each extraction run produces a consolidated text output file (combined_output.txt) and can also generate per-file text outputs as well as a detailed JSONL index containing metadata.

The script provides extensive logging and progress tracking, recording every action to both the console and a log file (pdf_extraction.log). Users can specify page ranges, choose between extraction engines, enable/disable OCR and fine-tune performance with worker counts.

Whether used for lightweight text gathering or large-scale document processing, PDF-To-TXT delivers a reliable and transparent workflow with detailed logs, clear error reporting and flexible configuration options.

Below are instructions for installing and running this application on a Linux machine.

• Git

• Python

1. Install the above programs

2. Open a terminal

3. Clone this repository: git clone git@github.com:devbret/pdf-to-txt.git

4. Navigate to the repo's directory: cd pdf-to-txt

5. Create a virtual environment: python3 -m venv venv

6. Activate your virtual environment: source venv/bin/activate

7. Install the needed dependencies: pip install -r requirements.txt

8. Place your PDF files in the input directory of this repo

9. Run the application: python3 app.py

10. When finished, exit the virtual environment: deactivate

This project repo is intended to demonstrate an ability to do the following:

• Extract text from any number of PDF files in bulk and consolidate the results into a single .txt file

• Choose between the pypdf and pdfminer.six extraction engines

• Process PDFs in parallel across multiple CPU cores, with configurable worker counts and page-range selection

• Record runs in a detailed log file and an optional JSONL metadata index and finish with a summary

If you have any questions or would like to collaborate, please reach out either on GitHub or via my website.