Unlimited OCR: One-shot long-horizon parsing
github.com
github.com
The way I understand this works is that the researchers found a clever architectural hack to stop AI from hoarding memory when reading long documents.
Normally, when an AI transcribes a 100 page PDF, it tries to remember every single word it has already ingested. This short-term memory (the KV cache) grows linearly O(N) until the model runs out of VRAM and crashes (or caps it) To avoid this, developers are forced to build janky code that chops PDFs into individual pages, processes them one by one, and glues the text back together.
Unlimited OCR uses Reference Sliding Window Attention (R-SWA) to split the AI's focus into two paths:
Global Reference: The AI keeps full, uncompromised sight of the original document image so it never loses context.
Local Generation: The AI restricts its memory of its own typed text to a tight, moving window (like the last 128 words) and safely forgets the rest.
Will be very interesting for local AI and can’t wait to see what the community builds and extends with it!
Knowing algorithms, data structures and their memory and time complexities is very relevant for SWE. I've had teammates that didn't understand them and everything was fine until when it wasn't (scaling and performance issues).
Or, as I put it to a teammate: "Would you rather review the PR of someone that understands the difference between a set and a list or the PR of someone who doesn't?". This was after we interviewed a candidate with ~15 YoE, on paper, that didn't know the difference.
Agree with this; however knowing how to roll your own BFS/LRU/etc isn't -- in that case I'd rather review the PR of someone who understands how to leverage tested and known implementations than the PR of someone who decided to roll their own.
main comment was a bit tongue in cheek
You have the overriding context, facts that don't change very often at all. The participants names, their backgrounds etc.
Then you have some very fine grained facts (what they ate for breakfast this morning) which might be useful right now, but are irrelevant outside of a general trend over the longer term.
When trying to reconstruct a conversation you really need to find the right balance without pulling in everything that has ever been discussed.
This definitely is worth further investigation.
But for some reason the local models I used back then that was almost 2 years ago) weren't good enough so none of my optimizations did anything good for the translation quality.
There is a whole class of tricky documents. A decent (if you ignore the marketing bias) post about this problem can be found here:
[*] SWAG line height, trial and error to figure out the right amount of overlap given LLM error rates, etc.
Gemini has a very specific mode where it has been trained on making boxes normalized to a 1000x1000 grid (https://docs.cloud.google.com/gemini-enterprise-agent-platfo...) and in our experience this “just works” AND is very fast on 3.5 and 3.1 models without needing much thinking (so it is not terrifically expensive).
(BTW A+++ gold star triple thumbs up give this person a bonus to whomever did that magic it basically made this task for us tractable. When we first found it nobody else had anything like it - it’s worked so well I haven’t felt any need to look. )
So we say, “Hey Gemini draw box_2d […] around #{things we are interested in}” and then it is pretty easy to then go - ok if this is here and that is there, let’s slice the image in this particular way, making sure to overlap by some amount because the boxes are fuzzy, then send the chunks to a thing that turns it into JSON, then we use something like edge detection to reconstruct the whole from the parts. (Squint and it looks like whole genome shotgun sequencing)
A lower-tech version is to use a good detector and XY-cut or just a naive Y-cut or orientation-away cut to slice up the page. But if you're doing that you're getting closer and closer to DocVLM style OCR+low res image. Been playing around with something like this using the new PPOCRv6 which itself punches well above most traditional OCRs and is multi-language without the hassle of language detection and dict-loading for rec.
Were people really trying to do OCR with vanilla attention?
[^1] Although maybe I guess looking at their demo, tables that span multiple pages might be a use-case for having some look back.
I got digging into the state of optical music recognition and came away concluding that music is basically a greenfield for AI wherever you look. Optical music recognition is pretty terrible. AI understanding of music theory is terrible (actually looking at music that is; LLMs do okay at text descriptions of theory concepts where you can imagine some online texts making it in).
I think the issue is that we still don't have great digital formats that encode the dots on paper that musicians read. Music notation is pretty rich. Midi doesn't capture all of what's needed for symbolic understanding, because it was mostly made for capturing aspects relevant for playback or performance. MusicXML seems to be the closest for a digital format that encodes the information a musician would want, but there aren't great corpora of training data that would connect a MusicXML representation to sheet music images or to audio. I think that's because MusicXML falls short of encoding enough information to engrave music. Tools like MuseScore need to track a bunch of layout information that isn't encodable in MusicXML. Lilypond format is less verbose that MusicXML and contains a bit more information that is useful to the score creators, but most people don't create sheet music in lilypond. (As an aside, Lilypond bums me out with the state of jazz fonts. I hate looking at "legit" scores in jazz context)
I realize this is mildly off topic, but every time I see people making incremental gains on OCR, which to my mind is pretty good, I am reminded of how abysmal OMR is.
With the caveat that I'm not terribly fluent in ABC, it seems to me that simple things are simple, but hard things seem to be nearly pathological. And (again, maybe a lapse in my understanding) it seems like there may be a fair number of concepts that are impossible to convey in ABC?
Lastly, if I understand correctly, ABC got its start and is mostly popular as a simplified format for church songbooks. I'd imagine that would, uh, influence the training corpora towards sounding a bit... church songbooky.
EDIT: I may have been overly dismissive of ABC on first glance. It does seem like people have extended it quite a bit, and that it's at least, in theory, capable of encoding most of what I'd expect. And it's human readable, which is a benefit. Though, readability does take a stiff penalty the more richness you add (e.g. dynamics, articulations, stacked notes, etc)
ABC started very simple, because most of the performance information for folk music isn't written down, it's inferred by the player according to the idiom of that particular tradition. As usage of ABC has grown it's gotten more powerful but still falls far short of formats designed for western classical, like MusicXML or Lilypond.
To let LLMs compose music I chose json for context efficiency, but this seems like it could be better choice, simple, efficient, already a real format.
It may not be necessary…a lot of the training pairs/data for this could probably be procedurally created via code.
Would be pretty fun to work on and see it come to life.
On the topic of stem separation, I've wondered about creating a quasi-synthetic dataset by taking chunks of recordings by real musicians playing them back in a real space in various combinations and recording the resulting analog-blended cacophony. Could repeat in various environments like cathedrals, basement bars, etc for realism :-)
AIN'T THAT THE TRUTH.
My girlfriend is studying musicology and she has some physical disabilities that make it difficult for her to write things down sometimes. So I try to help her by writing some AI-powered TTS/OCR/etc. apps here and there. It becomes painfully obvious that music was never considered an important part of any AI training dataset, anywhere.
These days, I'm pleasantly surprised by how well Opus 4.8 understands/explains music theory (as you said). But ask him to transcribe/OCR/OMR some sheet music and he'll confidently give you the MusicXML/Lilypond equivalent of "2 + 2 = horse".
I really hope this ignored area will be swept up with the rest of the rising AI wave, but it's still criminally undervalued.
I've been thinking about what kind of organization could be self-sustaining and also produce good music AI training data as a natural byproduct. An ideal arrangement would be something that provided some incentive or benefit to musicians in exchange for their recorded interpretation of sheet music. Soundslice, mentioned by another user, seems to do that. They let both teachers and students upload recordings of music that has been turned into MusicXML. The recordings, paired to those snippets of sheet music, has to be a gold mine. Assuming they have enough users. If they aren't already working on stem separation and automatic transcription, they probably should be. Still, my hope would be to figure out some kind of sustainable model where that dataset could be created and released for open model development...
As a domain, I see AI in music as a boon to human creativity. I am very much a novice jazz improvisor, and a passable amateur technician on the trombone. Human instructors can do a lot for me, but there's a lot that is "grinding it out" repetition, where I think AI could be a huge aid. I heard Sam Harris on a podcast recently talk about his bullishness on the humanities (paraphrasing: people don't care if a human reads their MRI if detection is good, but people probably do care that a human wrote the novel they're reading).
Music might even be a better example of the irreplaceability of people. While some people might bop along to a tune composed by Suno on the radio, live music is just so much more enjoyable for me. And even better than listening to a live show played by masters, is playing together with friends. To the extent that AI can patiently help us learn the skills to express our own creativity, I'm here for it!
I always think of the nun character against AI in Mrs Davis:
> "Don't give it a name. No one calls Facebook Doug. No one calls Twitter Mary Lou. No one calls them anything, because no one uses them anymore. They use it, and it's not a person. It's code. - Mrs Davis
On the other hand if I try to talk to Facebook, all he says in response is "200 OK".
To understand why OMR is so neglected is because most people widely underestimate the difficulty of the task. It has a specific blend of the most extreme shapes combined with an extremely complicated graphical grammar...
My first thought was whether MEI format is being added to MuseScore (the sheet music editor I use these days). It looks like it is: https://music-encoding.org/musescore-doc/
As a somewhat related aside, now that the MuseScore people own Hal Leonard and seem to pushing integration with their cloud subscription service, I wonder if they'll see some of these directions as potentially competing with them. I don't think there's anyone who wouldn't love a transposable clean digital version of their Real Books... and if Hal Leonard is in the business of selling Real Books, I can see where good OMR might be a problem for them. I guess piracy of scanned versions is already rampant, so maybe it's a wash.
You can look at samples of Hal Leonard's Real Book(s) on their website to get a sense of what it looks like. Again, just an aesthetic preference, but one I and many others hold nonetheless.
A salient extract:
...but why is it so complicated? A novice interpretation of "music" is "a bunch of notes!" ... my amateur interpretation of "music" is "layers of notes".
You can either spam 100 notes in a row, or you effectively end up with:
melody = [ a, b, [c+d], e, ... ]
bassline = [ b, _, b, _, ... ]
music = melody + bassline
score = [
"a bunch of helper text",
+ melody,
+ bassline,
+ page_size, etc...
]
You might like the "iReal Pro" app for the replacement and transposition of jazz standards on your tablet. It's pretty great for that use case versus camera scans.
Class Act.
Any compliment can be repurposed as sarcasm, but it's obscenely cynical to immediately assume a compliment is sarcastic - instead of just a compliment. And by the way, there's no 'real' in the poster's message.
Cheers.
(As a side note, I do OCR locally as a small RAG for citations I read in books and also chunk input, but merely to save RAM - interesting this natural approach also work in a streaming model)
This approach feels like it could be used for image gen as well (in some combination). Read/view image, start drawing image using illustrator/inkscape/etc (or just SVG), then fill in with what was missed after
I also think the attention approach (always attend to the image/prefix, with a sliding window for local context) is neat!
I do wish they updated their comparison table to include more recent work (that scores marginally better on OmniDocBench), like dots.mocr.
A simple example is words that are supposed to be in other languages being automatically translated to English, which ruins the effect
In transcription, you want near certainty, or you want marking that the word could not be read with certainty - yes, context lets you guess, but you want - for some OCR - to know when it's a guess based on other than the letters in order forming a word.
Example, in a census document on familysearch.com the transcriber "corrected" a name as Joseph. The literal letters in the handwritten document spell Josepth ... and sure enough that's a local variant spelling (Eire).
In another document the writer has used "Joh" as an abbreviation, a [human, I assume] transcriber put that as John ... which is most likely, but happens to be wrong.
Sometimes you care that it's guessed, sometimes you want just the best guess.
A nitpick, because it's often a dogwhistle: but almost nobody in Ireland calls it that when speaking English. And that's still incorrect in Irish, the correct spelling is Éire.
Using Éire in English, would be seen as odd. You wouldn't say Deutschland or Danmark.
> If I change the rest of the sentence to Na Gaeilge will that be better.
No. And you've used the genitive instead of nominative there, so I have some doubts that you could.
1) I am German descent so I'd definitely use Deutschland to appear fancy or play with words when speaking of Germany, without any bias implied or meant.
2) The problem with believing in dogwhistles (whether they exist or not, and I know they do, but bear with me) is that the "perceived dogwhistle surface area" increases in proportion to your belief in the prevalence of dogwhistles. In other words, the more firmly you are looking for "plausibly deniable" racist terms, the more you will find terms that were actually intended to be innocent, to be offensive, and the more upset you will be in the world, AND the more annoyed people will get with you if they are not subscribed to the whole "we must avoid any possible term that could remotely be misconstrued as a plausibly-deniable dogwhistle for fear of offending someone" worldview.
I would have absolutely used Éire but in a friendly way, and you're saying it would be perceived as a dogwhistle. Best to clarify what the person who typed it meant, before jumping to conclusions, sir. Not everyone is interested in filling their mind with extra rules just to cater to others' insecurities.
Lastly, your comment violates the https://en.wikipedia.org/wiki/Principle_of_charity , which is a good principle for everyone to maintain.
It has converted about 200 pages in an hour.
Shouldn't Baidu (or Google) hoard it for themselves to extract the value in a way the competition isn't be able to imitate?
Employers get prestige (useful for the hiring funnel) and sometimes strategically disrupt competitors (e.g. Meta releasing Ollama)
This will help if you want to dig deeper - https://vectree.io/c/how-ocr-works-traditional-pipelines-vs-...
I would definitely understand post processing, like extracting data, answering question .. etc, but why re-doing the OCR engine itself?
"A widely held view is that employing a large language model (LLM) as the decoder allows the model to leverage the prior distribution of language, leading to improved OCR performance. However, the downside is equally evident: as the output sequence lengthens, the accumulated KV cache drives up memory consumption and progressively slows down generation."
CJK have lots of character and high confusion rate.
Arabic scripts are complex and have lots of morphs.
Vietnamese have easily confused diacritics.
Thai have lots of non-standard fonts.
In your opinion, what is SOTA here?
But if you are trying to ingest diverse documents with headings, multi-column layouts, headers and footers, ad space in the middle of your text, etc, vision-llms are a giant step forward. But you need the context of the previous page to make good decisions about the current page, which is where things quickly get janky (or slow, if you choose the naive approach)
Vision-llms also seem to deal much better with variance in scripts. Cursive, random Japanese in the middle of the text, weird math symbols, handwriting from three centuries ago, all "just works" without you even having to remember that this can happen
OCR still sucks in 2026. Hopefully this might improve the situation but I haven't tested it yet.
- marker (with --force-ocr) gives me the best results
- Mistral OCR (seems really great, but I never managed to get it work)
- Mathpix (tried a long time ago)
- docling (gives me garbage, I must use it wrong)
- Unlimited OCR (will try it)
- ???
- AWS Textract
We use it on 200 page IEEE standards that are notoriously complex, filled with tables and diagram. Highly reccomend.
Well... the idea seems to be (as far as I understand it, at least) that optical errors and artifacts can now be compensated as the OCR engine is now context-aware.
Say, for example, some random long ass name chemical. It's not going to be in a word correction database, but a context-aware engine (ideally, one that has been supplemented with chemistry data) can now correct "bad" reads of the chemical's name.
Of course, there remains the issue of how to prevent the infamous Xerox bug [1]...
[1] https://www.dkriesel.com/en/blog/2013/0802_xerox-workcentres...
What if the goal is something actually useful, such as converting scientific paper PDF back to LaTeX that renders into a pixel-perfect copy? What about converting tables from electronics datasheets into computer-readable form? I wouldn't even expect it in the next decade.
Last year I finally OCR'd many hundreds of pages of my father's old writings. I found that feeding it to Claude Sonnet 4.x via API gave me results that were perfect. No corrections required. So perfect, that Claude was reading along with the story, and actually pointed out a continuity error in the story where an incorrect character was reciting dialog. Claude asked if it should transcribe exactly as is or if I would like Claude to correct the continuity error.
Claude also correctly OCR'd some handwriting that was in the margins of the documents. Sonnet came very close to transcribing a Word Sleuth puzzle, but that was where I hit the limits of its capability at the time.
Mistral OCR was also good (and actually what I started with), but it wasn't quite as good as Claude. And when it was wrong, Mistral could be frighteningly wrong - one API call must have failed, the model must have been presented with a pure black / null image, and I got back a "transcription" that described neverending darkness. It read like something the Woodsman would have broadcast in Twin Peaks S3E8. That poor model.
Tables from electronics datasheets might be okay, I think I've had success with OCR of technical manuals with tables for 80s synthesizer hardware. But I admit my use cases don't crossover into transcriptions of equations or graphs.