PDF Watermark Remover 🚫🖼️

This Python script utilizes the power of PyMuPDF and PIL libraries to identify and remove the most frequently occurring image (often a watermark) from a PDF file. It's an efficient way to clean up your PDFs from unwanted images without compromising the integrity of the document.

Features ✨

• Extract Images: Scans through each page of the PDF to extract images.
• Image Comparison: Utilizes Structural Similarity Index (SSIM) to compare images and identify duplicates or near-duplicates.
• Remove Most Frequent Image: Identifies the most frequently occurring image across the document and removes it.
• Save Modified PDF: Outputs a new PDF file with the watermark removed.

Prerequisites 📋

Before you can run this script, you need to have Python installed on your machine along with the following Python libraries:

• PyMuPDF (Fitz)
• PIL (Pillow)
• NumPy
• scikit-image

You can install these dependencies using pip:

pip install -r requirements.txt

Usage 🚀

1. Place the PDF file you want to process in the same directory as the script or specify the path to the file.
2. Run the script with Python:

python main.py

3. The script will process the PDF, remove the most frequent image (watermark), and save a modified version of the PDF in the same directory.

How It Works 🛠️

1. Extracting Images: The script first extracts all images from the PDF.
2. Comparing Images: It then compares each image with every other image in the document to find duplicates based on SSIM.
3. Identifying and Removing the Watermark: The most frequently occurring image is considered the watermark and is removed from the document.
4. Saving the PDF: Finally, the script saves the modified PDF without the watermark.

Note 📝

• The script assumes that the most frequent image in the PDF is the watermark. This might not always be the case, so use it with caution.
• The effectiveness of the watermark removal can vary based on the quality and complexity of the images in the PDF.

Contribution 🤝

Feel free to fork this project and contribute. Whether it's adding new features, improving the documentation, or fixing bugs, your contributions are always welcome!

License 📄

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