DeepSeek OCR

LLMs are compute heavy with quadratic scaling (in compute) per tokens. They are trying to compress text tokens into visual tokens with their VLM.

Maybe they would render texts to an image before tokenizing to reduce the compute cost.

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 where is the juice being squeezed from, why does the vision token representation end up being more efficient than text tokens.

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.

I wonder if text written using chinese characters is more compatible with such vision centric compression than latin text.

Vision is how humans see text. So text must have built in adaptations to protect from visual noise. For example, two words that look similar must never appear in similar contexts, or else they would be conflated. Hence we can safely reduce such words to the same token. Or something like that.

>with one vision token you have one vector of N elements, but with textual tokens you have M vectors of N elements

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 << y (especially by a factor of 10 as quoted in the paper).

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?).

You should read the 6th page of the paper (and page 5 for architecture breakdown), they show that they are compressing the vision tokens with convolution to keep a strong semantic understanding and keep a small amount of tokens.

But I think it's still experimentall.

just a hunch but like, from something to do with Unicode?

The trick is that the vision tokens are continuous valued vectors, while the text tokens are elements from a small discrete set (which are converted into continuous valued vectors by a lookup table). So, vision tokens can convey significantly more bits per token than text tokens. This allows them to pack the content of multiple text tokens into a single vision token.

Is that really factual/true?

Lots of words have multiple meanings and can mean different things even if used in the same sentence/context just from the interpretation of the person reading it.

Heck, it'd argue that most (not all) dayjob conflicts are down to such differences in interpretation /miscommunications

That also works purely on text and it's the trick I used in my German speech recognition engine ( https://arxiv.org/abs/2206.12693 ).

"I'm studying at Oxford Univ" has basically no loss in meaning even though "University" was truncated to less than half its characters.

This is like how many CLIs accept the shortest unique version of commands.

A text token generally represents a portion of a single word, while a vision token represents a portion of the entire page, which may include multiple words. This is where the "compression factor" comes from.

The number of bits to represent a text or vision token is the same, since they are both represented as embeddings of a fixed number of dimensions defined by the Transformer (maybe a few thousand for a large SOTA model).

Whether a vision token actually contains enough information to accurately extract (OCR) all the text data from that portion of the image is going to depend on how many pixels that vision token represents and how many words were present in that area of the image. It's just like considering images of the same page of text at different resolutions - a 1024x1024 image vs a 64x64 one, etc. As the resolution decreases so will OCR accuracy. At some point the resolution is insufficient and the words become a blurry mess and OCR accuracy suffers.

This is what DeepSeek are reporting - OCR accuracy if you try to use a single vision token to represent, say, 10 text tokens, vs 20 text tokens. The vision token may have enough resolution to represent 10 tokens well, but not enough for 20.

I think it's not the case. Chinese characters have the highest information entropy of all writing systems. However, Chinese characters are all independent symbols, which means if you want the LLM to support 5000 Chinese characters, you need to put 5000 characters into the lookup table (obviously there's no root, prefix, and suffix in Chinese, you cannot split the character into multiple reusable word pieces). As a result, you may need fewer characters to represent the same meaning compared to latin languages, but LLMs may also need to activate more token embeddings.

Couldn't you do something like add a bidirectional encoder after your embedding look up table to compress your text into some smaller token-count semantic space before feeding your transformer blocks to get a similar effect, then?

Yes, you can get good compression of a long sequence of "base" text tokens into a shorter sequence of "meta" text tokens, where each meta token represents the information from multiple base tokens. But, grouping a fixed number of base tokens into each meta token isn't ideal, since that won't align neatly with sensible semantic boundaries, like words, phrases, sentences, etc. So, the trick is how decide which base tokens should be grouped into each meta token....

This sort of "dynamic chunking" of low-level information, perhaps down to the level of raw bytes, into shorter sequences of meta tokens for input to some big sequence processing model is an active area of research. Eg, one neat paper exploring this direction is: "Dynamic Chunking for End-to-End Hierarchical Sequence Modeling" [1], from one of the main guys behind Mamba and other major advances in state-space models.

[1] - https://arxiv.org/abs/2507.07955