[meander_water]

A couple of examples I have seen recently which makes me agree with OP:

- PaddleOCR, a 0.9B model that reaches SOTA accuracy across text, tables, formulas, charts & handwriting. [0]

- A 3B and 8B model which performs HTML to json extraction at GPT-5 level accuracy at 40-80x less cost, and faster inference. [1]

I think it makes sense to fine tune when you're optimizing for a specific task.

[0] https://huggingface.co/papers/2510.14528

[1] https://www.reddit.com/r/LocalLLaMA/comments/1o8m0ti/we_buil...

[soVeryTired]

Have you used PaddleOCR? I'm surprised they're claiming SOTA without comparing against Amazon Textract or Azure doc intelligence (LayoutLM v3 under the hood, as far as I know).

I've played around with doc recognition quite a bit, and as far as I can tell those two are best-in-class.

[mejutoco]

Amazon textract is not great at multi colum layouts in my experience. Docupanda or some azure models beat it. Just my 2 cents.

[alansaber]

This comes back to the SLM vs LLM debate (sizes in relative terms), where an SLM can be optimised for a specific task, and out-perform an LLM. But it's not worth it (time, effort) for most tasks unless 1. they are very sensitive to precision or 2. it is ultra-high volume.