A system that automatically detects advertisements in PDF editions of newspapers and makes them clickable, allowing readers to instantly visit the advertiser's website by clicking on an ad.
Many regional publishers in Germany (and across Europe) continue to release newspaper editions in PDF format. While PDFs are convenient for printing and archiving, they are not designed for modern digital media expectations.
The client faced several key challenges:
Project goal: automate the processing of PDF newspapers by detecting ad blocks, extracting links, and adding clickable areas directly into the original document. The solution needed to be fast, scalable, and fully autonomous.
We developed an end-to-end pipeline that transforms static PDFs into interactive documents without human intervention.
Source PDF files are uploaded to cloud storage (AWS S3 or equivalent). Each file receives a unique ID, and processing starts automatically.
Using a YOLO-based computer vision model, the system segments each page and identifies potential advertisement blocks. The model returns bounding box coordinates for each detected ad area.
Each detected block is saved as an image and sent to a multimodal language model (e.g., Gemini or a similar LLM). The model analyzes the content to:
The system inserts clickable hyperlinks into the original PDF. Each clickable region precisely matches the coordinates of the detected ad block, preserving the original layout and design.
The final interactive PDF is saved alongside the original file in storage. A processing log is also generated, including:

The solution automated a labor-intensive workflow and delivered measurable benefits:
The system was designed with scalability in mind and is ready for further development, including:
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