Hey everyone!
I wanted to share my first open-source project,*Chickadee Studio.
It's a desktop application I built to help identify potentially hidden or disguised advertising in website content, including posts, comments, and images.
The idea came from a problem I found interesting: not all advertising is immediately visible or easy to identify with simple keyword matching.
Some content uses look-alike characters, unusual Unicode combinations, text embedded in images, or even hidden HTML elements.
So I wanted to experiment with combining several techniques to analyze this kind of content.
What it does:
- Scans webpages, posts, comments, and images
- Uses OCR (PaddleOCR) to extract text from images
- Analyzes DOM elements and CSS for potentially hidden content
- Detects text obfuscation techniques
- Uses local ML models to help identify advertising context
- Provides screenshots and evidence for manual review
- Supports optional AI-assisted analysis
Tech stack: Python, Electron, React, Playwright, PaddleOCR, and scikit-learn.
The core analysis runs locally, and the project is available under the Apache 2.0 license.
It's still an early project, and I'm sure there are plenty of things that could be improved.
Since this is my first open-source release, I'd especially appreciate feedback on the architecture, detection approach, usability, or anything else that stands out.
GitHub: https://github.com/chickadee-project/chickadee_studio
Thanks for checking it out!
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