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DeepSeek OCR

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Re: DeepSeek OCR

#201

Post by throwaway314155 »

simonw wrote: post #199

I figured out how to get this running on the NVIDIA Spark (ARM64, which makes PyTorch a little bit trickier than usual) by running Claude Code as root in a new Docker container and having it figure it out. Notes here: https://simonwillison.net/2025/Oct/20/deepseek-ocr-claude-co... Here's a result I got https://github.com/simonw/research/blob/main/deepseek-ocr-nv... - against this image: https://static.simonwillison.n…

> by running Claude Code as root in a new Docker container

How do you get the "as root" part of that to work?

(sorry if it's explained in your article)

Re: DeepSeek OCR

#202

Post by kelvinjps10 »

pietz wrote: post #42

My impression is that OCR is basically solved at this point. The OmniAI benchmark that's also referenced here wasn't updated with new models since February 2025. I assume that's because general purpose LLMs have gotten better at OCR than their own OCR product. I've been able to solve a broad range of OCR tasks by simply sending each page as an image to Gemini 2.5 Flash Lite and asking it nicely to extract the content…

Google vision it's still better than Gemini at OCR, for example at getting bounding boxes.

Re: DeepSeek OCR

#203

Post by CaptainOfCoit »

coder543 wrote: post #193
CaptainOfCoit wrote: post #192

Earlier quoted context omitted.

Alright, so now the easy thing is done, now how do you actually manage them, keep them running and do introspection without resorting to SSH or even remote desktop?

How do you manage any EC2 instance “without resorting to SSH”? Even for Linux EC2 instances, the right answer is often tools like Ansible, which do still use SSH under the hood.

You usually provision them via images, that they then either install from or boot from directly. Not to mention there are countless of infrastructure software to run that works for at least Linux, sometimes Windows and seldom even macOS.

Re: DeepSeek OCR

#204

Post by kamranjon »

dumpsterkid wrote: post #154

I haven't fired this up yet to try but I've been evaluating & working with quite a few different VLMs from the small granite, qwen etc models up to the larger VLMs available to see if we can fully replace traditional OCR in our system but I've been disappointed so far - our system takes documents from customers and supplies them back normalized documents (i.e rasterized multi-page bitmaps) marked up as they've reques…

Have you tried moondream yet[1]? The moondream 3 preview model[2], according to the blogpost[3] appears to outperform many frontier models on VLM tasks and does so with a relatively small footprint.

[1] https://moondream.ai/

[2] https://huggingface.co/moondream/moondream3-preview

[3] https://moondream.ai/blog/moondream-3-preview

Re: DeepSeek OCR

#205

Post by kelvinjps10 »

wahnfrieden wrote: post #124
raincole wrote: post #47

Earlier quoted context omitted.

If you can accept that the machine just make up what it doesn't recognize instead of saying "I don't know," then yes it's solved. (I'm not being snarky. It's acceptable in some cases.)

Do any LLM OCRs give bounding boxes anyway? Per character and per block.

Gemini does but it's not as good as Google vision, and the format it's différent Here it's the documentation https://cloud.google.com/vertex-ai/generative-ai/docs/boundi...

Also Simon Willison Made a blog post that might be helpful https://simonwillison.net/2024/Aug/26/gemini-bounding-box-vi...

I hope that this capability improves so I can use only Gemini API.

Re: DeepSeek OCR

#206

Post by coder543 »

CaptainOfCoit wrote: post #203
coder543 wrote: post #193

Earlier quoted context omitted.

How do you manage any EC2 instance “without resorting to SSH”? Even for Linux EC2 instances, the right answer is often tools like Ansible, which do still use SSH under the hood.

You usually provision them via images, that they then either install from or boot from directly. Not to mention there are countless of infrastructure software to run that works for at least Linux, sometimes Windows and seldom even macOS.

I specifically mentioned the imaging capability of EBS for Mac, which you dismissed as the easy part. Now you’re claiming that is the main thing? Well, good news!

And yes, Ansible (among other tools) can be used to manage macOS.

This discussion doesn’t seem productive. You have a preconceived view point, and you’re not actually considering the problem or even doing 5 seconds of googling.

Managing a Mac fleet on AWS isn’t a real problem. If Apple’s OCR framework were significantly above the competition, it could easily be used. I would like to see benchmarks of it, as the other person was also asking for.

Re: DeepSeek OCR

#207

Post by stopyellingatme »

yoran wrote: post #19

How does an LLM approach to OCR compare to say Azure AI Document Intelligence ( https://learn.microsoft.com/en-us/azure/ai-services/document... ) or Google's Vision API ( https://cloud.google.com/vision?hl=en )?

Not sure about the others but we use Azure AI Document Intelligence and its working well for our resume parsing system. Took a good bit of tuning but we havent had to touch it for almost a year now.

Re: DeepSeek OCR

#208

Post by simonw »

throwaway314155 wrote: post #201
simonw wrote: post #199

I figured out how to get this running on the NVIDIA Spark (ARM64, which makes PyTorch a little bit trickier than usual) by running Claude Code as root in a new Docker container and having it figure it out. Notes here: https://simonwillison.net/2025/Oct/20/deepseek-ocr-claude-co... Here's a result I got https://github.com/simonw/research/blob/main/deepseek-ocr-nv... - against this image: https://static.simonwillison.n…

> by running Claude Code as root in a new Docker container How do you get the "as root" part of that to work? (sorry if it's explained in your article)

Run it on a root account and do:

  IS_SANDBOX=1 claude --dangerously-skip-permissions

Re: DeepSeek OCR

#209

Post by blindriver »

pietz wrote: post #42

My impression is that OCR is basically solved at this point. The OmniAI benchmark that's also referenced here wasn't updated with new models since February 2025. I assume that's because general purpose LLMs have gotten better at OCR than their own OCR product. I've been able to solve a broad range of OCR tasks by simply sending each page as an image to Gemini 2.5 Flash Lite and asking it nicely to extract the content…

I attempted OCR using all of the open source models available about 3 months ago, including Llama 4. These were pngs of text using a regular font. Most produced garbage except Llama 4, and even then it was only about 90% accurate. Using OpenAI or Gemini produced much better results but the open source models were really bad.

Re: DeepSeek OCR

#210

Post by cheema33 »

ozgune wrote: post #30
yoran wrote: post #19

How does an LLM approach to OCR compare to say Azure AI Document Intelligence ( https://learn.microsoft.com/en-us/azure/ai-services/document... ) or Google's Vision API ( https://cloud.google.com/vision?hl=en )?

OmniAI has a benchmark that companies LLMs to cloud OCR services. https://getomni.ai/blog/ocr-benchmark (Feb 2025) Please note that LLMs progressed at a rapid pace since Feb. We see much better results with the Qwen3-VL family, particularly Qwen3-VL-235B-A22B-Instruct for our use-case.

Omni OCR team says that according to their own benchmark, the best OCR is the Omni OCR. I am quite surprised.
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