Earlier quoted context omitted.
Cost, throughput, latency...
Traditional OCR is faster, cheaper, and much more reliable than LLMs
I wish it were. Alas...
Unlimited OCR: One-shot long-horizon parsing
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Re: Unlimited OCR: One-shot long-horizon parsing
#22OCR has been solved long time ago with vision models. Solutions are consistent, reliable, and stable. What is the point of reinventing the wheel? I would definitely understand post processing, like extracting data, answering question .. etc, but why re-doing the OCR engine itself?
I've been working on Parseur for the last 10 years, and OCR has not been solved yet, let me tell you.
OCR still sucks in 2026. Hopefully this might improve the situation but I haven't tested it yet.
Re: Unlimited OCR: One-shot long-horizon parsing
#23my attempts at using AI to do OCR have always resulted in invented artifacts, which is not production feasible. does this suffer from that as well?
A simple example is words that are supposed to be in other languages being automatically translated to English, which ruins the effect
Re: Unlimited OCR: One-shot long-horizon parsing
#24OCR has been solved long time ago with vision models. Solutions are consistent, reliable, and stable. What is the point of reinventing the wheel? I would definitely understand post processing, like extracting data, answering question .. etc, but why re-doing the OCR engine itself?
Real question: what tool do you use? (for long/complex documents with tables, code, maths)
- 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)
- ???
Re: Unlimited OCR: One-shot long-horizon parsing
#25"We would like to thank Deepseek-OCR, Deepseek-OCR-2, PaddleOCR for their valuable models and ideas." Class Act.
I don’t understand the shade being thrown ?
It's the opposite of shade, unless GP is being sarcastic. "Class act" is normally a compliment, and in the context here it sounds to me like they're congratulating Baidu/the researchers in being transparent about where their ideas came from.
Re: Unlimited OCR: One-shot long-horizon parsing
#26OCR has been solved long time ago with vision models. Solutions are consistent, reliable, and stable. What is the point of reinventing the wheel? I would definitely understand post processing, like extracting data, answering question .. etc, but why re-doing the OCR engine itself?
> I would definitely understand post processing, like extracting data, answering question .. etc, but why re-doing the OCR engine itself?
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...
Re: Unlimited OCR: One-shot long-horizon parsing
#27What are the requirements for running this locally?
Re: Unlimited OCR: One-shot long-horizon parsing
#28We've invented chunking? We are so back.
Re: Unlimited OCR: One-shot long-horizon parsing
#29I recently bought a tablet for sheet music, mostly to replace a stack of jazz "Real Books" at jam sessions. And the phone camera scans I made are okay, but fixed in size and have a lot of artifacts. And it would be great to transpose on the fly for e.g. Bb or Eb instruments, but being a scan this is obviously not possible.
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.
Re: Unlimited OCR: One-shot long-horizon parsing
#30Very interesting. 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, develop…
See, leetcode is useful. As I do this leetcode grind, I’ve been why techniques exist / how they’re used irl. Lots of interesting stuff there