A bit ago I tried throwing a couple of random simple Japanese comics (think 4koma but I don't think either of the ones I threw in were actually 4 panels) from Pixiv into Gemma 3b on AI studio.
- It transcribed all of the text, including speech, labels on objects, onomatopoeias in actions, etc. I did notice a kana was missing a diacritic in a transcription, so the transcriptions were not perfect, but pretty close actually. To my eye all of the kanji looked right. Latin characters already OCR pretty well, but at least in my experience other languages can be a struggle.
- It also, unprompted, correctly translated the fairly simple Japanese to English. I'm not an expert, but the translations looked good to me. Gemini 2.5 did the same, and while it had a slightly different translation, both of them were functionally identical, and similar to Google Translate.
- It also explained the jokes, the onomatopoeias, etc. To my ability to verify these things they seemed to be correct, though notably Japanese onomatopoeias used for actions in comics is pretty diverse and not necessarily super well-documented. But contextually it seemed right.
To me this is interesting. I don't want to anthropomorphize the models (at least unduly, though I am describing the models as if they chose to do these things, since it's natural to do so) but the fact that even relatively small local models such as Gemma can perform tasks like this on arbitrary images with handwritten Japanese text bodes well. Traditional OCR struggles to find and recognize text that isn't English or is stylized/hand-written, and can't use context clues or its own "understanding" to fill in blanks where things are otherwise unreadable; at best they can take advantage of more basic statistics, which can take you quite far but won't get you to the same level of proficiency at the job as a human. vLLMs however definitely have an advantage in the amount of knowledge embedded within them, and can use that knowledge to cut through ambiguity. I believe this gets them closer.
I've messed around with using vLLMs for OCR tasks a few times primarily because I'm honestly just not very impressed with more traditional options like Tesseract, which sometimes need a lot of help even just to find the text you want to transcribe, depending on how ideal the case is.
On the scale of AI hype bullshit, the use case of image recognition and transcription is damn near zero. It really is actually useful here. Some studies have shown that vLLMs are "blind" in some ways (in that they can be made to fail by tricking them, like Photoshopping a cat to have an extra leg and asking how many legs the animal in the photo has; in this case the priors of the model from its training data work against it) and there are some other limitations (I think generally when you use AI for transcription it's hard to get spatial information about what is being recognized, though I think some techniques have been applied, like recursively cutting an image up and feeding it to try to refine bounding boxes) but the degree to which it works is, in my honest opinion, very impressive and very useful already.
I don't think that this demonstrates that basic PDF transcription, especially of cleanly-scanned documents, really needs large ML models... But on the other hand, large ML models can handle both easy and hard tasks here pretty well if you are working within their limitations.
Personally, I look forward to seeing more work done on this sort of thing. If it becomes reliable enough, it will be absurdly useful for both accessibility and breaking down language barriers; machine translation has traditionally been a bit limited in how well it can work on images, but I've found Gemini, and surprisingly often even Gemma, can make easy work of these tasks.
I agree these models are inefficient, I mean traditional OCR aside, our brains do similar tasks but burn less electricity and ostensibly need less training data (at least certainly less text) to do it. It certainly must be physically possible to make more efficient machines that can do these tasks with similar fidelity to what we have now.