Show HN: OCR Arena – A playground for OCR models

What is needed to evaluate OCR for most business applications (above everything else) is accuracy.

Some results look plausible but are just plain wrong. That is worse than useless.

Example: the "Table" sample document contains chemical substances and their properties. How many numbers did the LLM output and associate correctly? That is all that matters. There is no "preference" aspect that is relevant until the data is correct. Nicely formatted incorrect data is still incorrect.

I reviewed the output from Qwen3-VL-8B on this document. It contains numbers in the wrong rows. I presume using its output would be incredibly dangerous. It should not be used for this purpose. There is no winning aspect to it. Does another model produce worse results? Then both models should be avoided at all costs.

Are there models available that are accurate enough for this purpose? I don't know. It is very time consuming to evaluate. This particular table seems pretty legible. A real production grade OCR solution should probably need a 100% score on this example before it can be adopted. The output of such a table is not something humans can realistically review. It either needs to work completely, or it does not work at all.

I am confident we'll reach a point where a mix of traditional OCR and LLM models can produce correct and usable output. But I would welcome a benchmark where (objective) correctness is rated separately from of the (subjective) output structure.

I've been really impressed with this model specifically because of how insanely cheap it is: https://replicate.com/ibm-granite/granite-vision-3.3-2b

I didn't expect IBM to be making relevant AI models but this thing is priced at $1 per 4,000,000 output tokens... I'm using it to transcribe handwritten input text and it works very well and super fast.

I suggest you make explicit the assumption that this website is specifically about English text. Otherwise the leaderboard is pretty meaningless, with extreme differences in performance across other scripts - and potentially even languages such as Vietnamese or Czech which use Latin but have lots of accents.

Interesting that the 8B of the Qwen3-VL family 9th place, above a few proprietary models. This thing can run locally with llama.cpp on modest hardware.

Offtopic, but what's the best OCR that can run offline on browsers with js/wasm with reasonable CPU/memory cost?

Working on a hobby project that interacts with user handwriting on <canvas>. Tried some CNN models for digits but had trouble with characters.

There have been such a large number of OCR tools pop up over the past ~year; sorely in need for some benchmarks to compare them. Would love to see support for normal OCR tools like tesseract, EasyOCR, Microsoft Azure, etc. I'm using these for some projects, and my experiments with VLMs for OCR have resulted in too much hallucination for me to switch. Benchmarks comparing across this aisle would be incredibly useful.

A limitation of this leaderboard approach that I want to point out is that while the large general-purpose LLMs can make greater leaps of inference (on handwriting and poor quality scans), and almost always produce better layouts and more coherent output, they can also sometimes be less correct. My experience is that they're more prone to skipping or transposing sections of text, or even hallucinating completely incorrect output, than the purpose-trained models. (A similar comparison can be made in turn to the character- or word-based OCR approaches like Tesseract, which are even less "intelligent" but also even less prone to those malbehaviors.)

Also, some of the models are prone to infinite loops and I suspect this is not being punished appropriately; the frontend seems to get into a bad state after around 50k characters, which prevents the user from selecting a winner. Probably would be beneficial to make sure every model has an output length limit.

Still, a really cool resource - I'm looking forward to more models being added.

Love this! Would have liked to see something like textract for a pre-LLM benchmark (but of course that's expensive), and also a distinction between handwritten text and printed one.

But still, this is incredibly useful!

Any plans to add Document Pre-trained transformer-2 (DPT-2) from https://landing.ai/?

Two suggestions:

UX on mobile isn’t great. It wasn’t obvious to me where the second model output was and I was thrown off even more so because the option to vote for model 1 output was presented without ever even seeing model two output.

Second suggestion would be to install a MathJax plugin so one can properly rate mathematical equations and formulas. Raw LATeX is easy to mistake and it makes comparing between LATeX and Unicode outputs hard.

Really like the idea. Unfortunately, my first upload is still spinning on one of the models about 5 minutes in. Clicking "Stop Battle" seems to do nothing either

Would be great to compare these against Apple’s LiveText. This project now supports it: https://github.com/mkyt/OCRmyPDF-AppleOCR

I’ve had great results locally. Albeit you need macOS >=13 for this.

Really hope there is a layout mode or ocr with bbox mode, I want to see the model restore the whole page.

We need to see Landing.ai DPT-2, from my tests its the best in term of ability to extract structure from complex tables so far.

This needs a "both are bad" button. There are some generations where I cannot rightfully beats the other.

Most of these are general LLM’s and not specifically OCR models. Where is Google Vision, Mistral, Paddle, Nanonets, or Chandra??

This is super helpful :) Curious about Grok as well!

FYI one of the models on the battle was pretty slow to load. Are these also being rated on latency or just quality?

I would be curious to see how Sonnet does. Their models are pretty solid when it comes to PDFs

Opus is multimodal??

Please add Chandra by Datalab

Claude would be good!

[under-the-rug stub]

[see https://news.ycombinator.com/item?id=45988611 for explanation]