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DeepSeek OCR

github.com

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Re: DeepSeek OCR

#51

Post by sramam »

carschno wrote: post #45
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…

Technically not OCR, but HTR (hand-written text/transcript recognition) is still difficult. LLMs have increased accuracy, but their mistakes are very hard to identify because they just 'hallucinate' text they cannot digitize.

Interesting - have you tried sending the image and 'hallucinated' text together to a review LLM to fix mistakes?

I don't have a use case of 100s or 1000s of hand-written notes have to be transcribed. I have only done this with whiteboard discussion snapshots and it has worked really well.

Re: DeepSeek OCR

#52

Post by maxloh »

CloseChoice wrote: post #21

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

Model weights are MIT too: https://huggingface.co/deepseek-ai/DeepSeek-OCR/blob/main/LI...

Re: DeepSeek OCR

#53

Post by mormegil »

carschno wrote: post #45
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…

Technically not OCR, but HTR (hand-written text/transcript recognition) is still difficult. LLMs have increased accuracy, but their mistakes are very hard to identify because they just 'hallucinate' text they cannot digitize.

This. I am reading old vital records in my family genealogy quest, and as those are sometimes really difficult to read, I turned to LLMs, hearing they are great in OCR. It’s been… terrible. The LLM will transcribe the record without problems, the output seems completely correct, a typical text of a vital record. Just… the transcribed text has nothing to do with my specific record. On the other hand, transkribus.eu has been fairly usable for old vital record transcription – even though the transcribed text is far from perfect, many letters and words are recognized incorrectly, it helps me a lot with the more difficult records.

Re: DeepSeek OCR

#54

Post by jakewins »

raincole wrote: post #47
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…

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.)

But this was very much the case with existing OCR software as well? I guess the LLMs will end up making up plausible looking text instead of text riddled with errors, which makes it much harder to catch the mistakes, in fairness

Re: DeepSeek OCR

#55

Post by darkwater »

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…

So, the mug with inspirational text says "Bountiful Potential"?

Re: DeepSeek OCR

#56

Post by ekianjo »

jmkni wrote: post #50
peter-m80 wrote: post #48

Earlier quoted context omitted.

No way it's solved. try to make OCR over a magazine with creative layouts. Not possible. I have a collection of vintage computer magazines and from time to time I try to OCR them whith the state of the art mechanisms. All of them requiere a lot of human intervention

do you have an example of a particularly tricky one?

Just try old ads you will see how hard it gets

Re: DeepSeek OCR

#57

Post by kbumsik »

bobsmooth wrote: post #49
coulix wrote: post #46

Earlier quoted context omitted.

This. Any complex parent table span cell relationship still has low accuracy. Try the reverse, take a complex picture table and ask Chatgpt5, claude Opus 3.1, Gemini Pro 2.5 to produce a HTML table. They will fail.

Maybe I misunderstood the assignment but it seems to work for me. https://chatgpt.com/share/68f5f9ba-d448-8005-86d2-c3fbae028b... Edit: Just caught a mistake, transcribed one of the prices incorrectly.

Right, I wouldn't use full table detection to VLM model because they tend to mistake with numbers in table...

Re: DeepSeek OCR

#59

Post by pietz »

carschno wrote: post #45
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…

Technically not OCR, but HTR (hand-written text/transcript recognition) is still difficult. LLMs have increased accuracy, but their mistakes are very hard to identify because they just 'hallucinate' text they cannot digitize.

We ran a small experiment internally on this and it looked like Gemini is better at handwriting recognition than I am. After seeing what it parsed, I was like "oh yeah, that's right". I do agree that instead of saying "Sorry, I can't read that" it just made up something.

Re: DeepSeek OCR

#60

Post by cahaya »

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…

Lot's of OCR/ LLM's (Even Gemini Pro 2.5) still struggle converting complex tables to markdown or HTML: Tables with multiple headers and merged cells that get mixed up, multiple columns with tick boxes get mixed up, multi page tables that are not understood correctly. Also Llamaindex fails miserably on those things.

Curious to hear which OCR/ LLM excels with these specific issues? Example complex table: https://cdn.aviation.bot/complex-tables.zip

I can only parse this table correctly by first parsing the table headers manually into HTML as example output. However, it still mixes up tick boxes. Full table examples: https://www.easa.europa.eu/en/icao-compliance-checklist

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