Detecting *whether* text exists in an image? [D]

Hi, I was looking to be able to very quickly detect *whether* text exists in an image (binary classification). Being a simple-ish task, there isn't substantial dedicated research on it, so I was looking for adjacent topics or models, but I'm not sure of any. I know there's an issue of scale tolerance, so I was like hmm FPN, but now I'm curious why absolutely no classification papers use FPN.

What do you guys think the best architectural approach? I’m probably using the pretrained PaddleOCR v6 detection backbone (LCNetv4) and fine tune on my domain (2D art text, vast scale variation, style variation, etc., 1920x1080 images). Only paper I've seen uses two feature maps and a grid approach where if any grid cell is a yes the whole image is classified as a yes. However, one could also use the simple global average (max pool better in this case maybe) to linear approach. There's also the issue of what's best if our data is only yes/no labels and not bounding boxes (the grid approach doesnt work then), I'd like to know how much this would actually affect a binary classification task and how the approach would change.

I feel like the answers might be test multiple ones but I’d like to hear some ideas or anything that could be useful that I could try out.