I’ve also heard very good things about these two in particular:
- LightOnOCR-2-1B: https://huggingface.co/lightonai/LightOnOCR-2-1B
- PaddleOCR-VL-1.5: https://huggingface.co/PaddlePaddle/PaddleOCR-VL-1.5
The OCR leaderboards I’ve seen leave a lot to be desired.
With the rapid release of so many of these models, I wish there were a better way to know which ones are actually the best.
I also feel like most/all of these models don’t handle charts, other than to maybe include a link to a cropped image. It would be nice for the OCR model to also convert charts into markdown tables, but this is obviously challenging.
This project has been pretty easy to build with agentic coding. It's a Frankenstein monster of glue code and handling my particular domain requirements, so it's not suitable for public release. I'd encourage some rapid prototyping after you've spent an afternoon catching up on what's new. I did a lot of document OCR and post-processing with commercial tools and custom code 15 years ago. The advent of small local VLMs has made it practical to achieve higher accuracy and more domain customization than I would have previously believed.
[1] If you're building an advanced document processing workflow, be sure to read the post-processing code in the GLM code repo. They're doing some non-trivial logic to fuse layout areas and transform text for smooth reading. You probably want to store the raw model results and customize your own post-processing for uncommon languages or uncommon domain vocabulary. Layout is also easier to validate if you bypass their post-processing; it can make some combined areas "disappear" from the layout data.
I remember that one clearing the scoreboard for many years, and usually it's the one I grab for OCR needs due to its reputation.
My documents have one or two-column layouts, often inconsistently across pages or even within a page (which tripped older layout detection methods). Most models seem to understand that well enough so they are good enough for my use case.
The new models are similarly better compared to tesseract v4. But what I'll say is that don't expect new models to be a panacea for your OCR problems. The edge case problems that you might be trying to solve (like, identifying anchor points, or identifying shared field names across documents) are still pretty much all problematic still. So you should still expect things like random spaces or unexpected characters to jam up your jams.
Also some newer models tend to hallucinate incredibly aggressively. If you've ever seen an LLM get stuck in an infinite, think of that.
Also, do you have preferred OCR models in your experience? I've had some success with dots.OCR, but I'm only beginning to need to work with OCR.
Not for OCR.
Regardless of how much some people complain about them, I really do appreciate the effort Artificial Analysis puts into consistently running standardized benchmarks for LLMs, rather than just aggregating unverified claims from the AI labs.
I don't think LMArena is that amazing at this point in time, but at least they provide error bars on the ELO and give models the same rank number when they're overlapping.
> Also, do you have preferred OCR models in your experience?
It's a subject I'm interested in, but I don't have enough experience to really put out strong opinions on specific models.
I think a more accurate reflection of the current state of comparisons would be a real-world benchmark with messy/complex docs across industries, languages.
It also doesn't provide error bars on the ELO, so models that only have tens of battles are being listed alongside models that have thousands of battles with no indication of how confident those ELOs are, which I find rather unhelpful.
A lot of these models are also sensitive to how they are used, and offer multiple ways to be used. It's not clear how they are being invoked.
That leaderboard is definitely one of the ones that leaves a lot to be desired.
I've thought of open sourcing the wrapper but havent gotten around to it yet. I bet claude code can build a functioning prototype if you just point it to "screen_ai" dir under chrome's user data.
How fast was it per page? Do you recall if it's CPU or GPU based? TY!
Any idea what model is being used?
And here's the kicker. I can't afford mistakes. Missing a single character or misinterpreting it could be catastrophic. 4 units vacant? 10 days to respond? Signature missing? Incredibly critical things. I can't find an eval that gives me confidence around this.
But, as others said, if you can't afford mistakes, then you're going to need a human in the loop to take responsibility.
I can feed it a multiple page PDF and tell it to convert it to markdown and it does this well. I don't need to load the pages one at a time as long as I use the PDF format. (This was tested on A.i. studio but I think the API works the same way).
How many pages did you try in a single request? 5? 50? 500?
I fully believe that 5 pages of input works just fine, but this does not scale up to larger documents, and the goal of OCR is usually to know what is actually written on the page... not what "should" have been written on the page. I think a larger number of pages makes it more likely for the LLM to hallucinate as it tries to "correct" errors that it sees, which is not the task. If that is a desirable task, I think it would be better to post-process the document with an LLM after it is converted to text, rather than asking the LLM to both read a large number of images and correct things at the same time, which is asking a lot.
Once the document gets long enough, current LLMs will get lazy and stop providing complete OCR for every page in their response.
One page at a time keeps the LLM focused on the task, and it's easy to parallelize so entire documents can be OCR'd quickly.
I never tested Gemini 3 PDF OCR compared to individual images but I can say it processes a small 6 page PDF better than the retired Gemini 1.5 or 2 did individual images.
I agree that OCR and analysis should be two separate steps.
Isn’t this close to the error rate of human transcription for messy input, though? I seem to remember a figure in that ballpark. I think if your use case is this sensitive, then any transcription is suspicious.
EDIT: https://github.com/overcuriousity/pdf2epub looks interesting.
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.