Off the top of my head: for a lot of OCR tasks, it’s kind of worse for the model to be smart. I don’t want my OCR to make stuff up or answer questions — I want to to recognize what is actually on the page.
I am reminded it's basically impossible to read cursive writing in a language you don't know even if it's the same alphabet.
Evaluation methods, too, are bad because they don't think critically about what the downstream task is. Word Error Rate and Character Error Rate are terrible metrics for most historical HTR, yet they're what people use because of habit.
It's a bit like how for a long time BLEU was the metric for translation quality. BLEU is based on N-gram similarity to a reference translation, so naturally translation methods based on and targeting N-gram similarity (e.g. pre NN Google translate) did well, and looked much better than they actually were.
Also, there are generalist models that have enough of a grasp of a dozen or so languages that fit comfortably in 7B parameters. Like the older Mistral, which had the best multi-lingual support at the time, but newer models around that size are probably good candidates. I am not surprised that a multilingual specialised model can fit in 8B or so.