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TLDR: It's MIT licensed
How good is this compared to most commercial OCR software?
Any vision model is better than commercial OCR software.
>先天下之忧而忧
Google translated this to "Worry about the world first" while Bing says "Worry before the worries of the world."
Can anyone shed some light on this saying or why it's in the article?
Earlier quoted context omitted.
But naively wouldn't you expect the representation of a piece of text in terms of vision tokens to be roughly the same number of bits (or more) than the representation as textual token? You're changing representation sure, but that by itself doesn't give you any compute advantages unless there is some sparsity/compressability you can take advantage of in the domain you transform to right? So I guess my question is wh…
Vision tokens are a good compression medium because with one vision token you have one vector of N elements, but with textual tokens you have M vectors of N elements, because one vision token represent multiple pixels (and possibly multiple words). This is why its a good compression medium for compute. It will never be as precise as textual tokens but it can be really good as they show in the paper.
Each vision token represents a 16x16 patch, but to fully cover a word you might need multiple vision tokens. So assuming that the embedding size of the vision token and text token is the same `d` (which I think has to be the case for multimodal models), then wouldn't the fair comparison be `x * d` elements for a sentence in terms of vision tokens, and `y * d` for the same sentence in terms of text tokens? I don't see how you could see a priori that x That said, if I do experimentally try this by shrinking this very comment down to the smallest font size I can read it at, then seeing how many 16x16 tokens it takes, you can fit more text than I expected in each "vision token". So I can maybe buy that x is at least not greater than y. But it can't be as simple as "each vision token can cover more text", since that only enables better compression if the encoder can actually uncover some sort of redundancy within each token. (And presumably the type of redundancy it uncovers probably isn't something that "classical" compression techniques can exploit, otherwise it seems like it would have been tried by now?).
It's deepseek so one can expect an open-source license but for anyone (like me) who wants to see that explicitly, since it's not obvious in the GitHub repo: https://huggingface.co/deepseek-ai/DeepSeek-OCR/blob/main/LI... TLDR: It's MIT licensed
Literally says MIT license on the right sidebar and in the readme tab and in the file called LICENSE
>先天下之忧而忧 How is this an example of a prompt? Google translated this to "Worry about the world first" while Bing says "Worry before the worries of the world." Can anyone shed some light on this saying or why it's in the article?
>先天下之忧而忧 How is this an example of a prompt? Google translated this to "Worry about the world first" while Bing says "Worry before the worries of the world." Can anyone shed some light on this saying or why it's in the article?
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 )?
>先天下之忧而忧 How is this an example of a prompt? Google translated this to "Worry about the world first" while Bing says "Worry before the worries of the world." Can anyone shed some light on this saying or why it's in the article?
Both translations don't catch the meaning well though. It means: "worry before the rest of the world (notice that they have something to) worry." The next part is 後天下之樂而樂("be happy only after the rest of the world is happy.")
I don't know why it's a prompt example.
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 )?
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.