I hope this question is not too broad or general. I have a very large set of images all of which contain text (some have more, some less). All of them have been tagged as containing, say, English text or Korean. I wonder if convolutional neural networks would be a good approach to classify these images as containing English vs. Korean. Or is there any existing literature/method that does this already. Crucially though, I am not interested in "understanding" the text, so this is not an NLP task but, I suppose, a task of classifying orthographies in the images.
This sounds like a fairly straightforward task, with low risks. I think the proper term is that you're you are trying to detect the script, which would be either Latin ( "English") or Hangul ("Korean"). The chance is that you end up learning fonts, though.