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.

• 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.

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

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.

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.

• 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.

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!

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