I've got a scan from a book that I OCR with new releases. Ligatures, critical sigla, Fraktur letterforms, subscripts, superscripts, etc.
Nothing special about this model for overly-detailed work like mine.
It's been a while since I last tested (and discontinued my subscription), but the "pro" models from OpenAI dominate. Not surprising, given the price difference, but it would be nice if an OCR-specific model could perform better. It's worth mentioning that even the highest-end models do a pretty poor job with intricate text like mine.
I have been quite happy with Mistral OCR for the documents I needed to process (typeset, but old, with questionable scan quality, sometimes elaborate typesetting or, much worse, typewriter-and-handwriting approximations of it). I do not test every new model when they are released, but I did a review shortly after Mistral OCR 3 was released and it was a very good compromise: cheap, fast, and good results without further processing. I found generalist models to be way too much faf to get them to avoid unnecessary modifications to the text and report accurate bounding boxes for figures and tables.
That said, models have sometimes surprising weaknesses and a model could be terrible overall but magically work for one type of document.
I got the opposite experience very recently : tried to OCR a bunch of handwritten emails addresses with chatGPT and I had to make so many corrections that I gave up. Whereas Mistral nailed it on first pass.
Yet: how is pricing?
Evaluating contents and routing appropriately isn't a new challenge in OCR, one of the oldest fields of applications in ML.
Thus, how do the smaller open models perform in tandem with relatively pricy $/pg models & APIs?
Your use case is remarkably rare relative to the volume and price sensitivity of enterprise data warehouse ops.
> the "pro" models from OpenAI dominate. Not surprising considering the price difference, but it would ne nice if an OCR-specific model could do better.
I haven't been impressed with any of Mistral's models. They obviously realized that they couldn't compete at the frontier so they decided to go for smaller focused models but even those have not been that good.
At this point I lost all hope for Europe playing any significant role in the AI race. If that’s a good or a bad thing I don’t know, but it seems to me like that’s the reality.
True, but that isn't EU/US/China, it's OpenAI/Anthropic/Grok/ …/DeepMind (based in UK)/… DeepSeek
With a lot of Chinese nationals in American companies, and an American corporation owning DeepMind, and a lot of people very upset with all of them at the same time, this is very messy.
? It absolutely is a race. Whether thats a positive thing or not is debatable but every lab is definitely in a race. What prize do you win? Imagine a world where only one country has AGI/ASI. Or a world where Europe only gets access to frontier models 6 months later. Far from ideal.
> What prize do you win? Imagine a world where only one country has AGI/ASI.
"Winning the race" doesn't give you that in any meaningful capacity. It gives you, at the absolute most, a temporary window where that's the case. See: nuclear weapons.
A reductive equation, economics isn't thermodynamics. Money is fictional and value is subjective and unstable. Within this context, being first to AGI means nothing if the second invention of it comes 2 months later and works an order of magnitude faster than what the first iteration had self-improved to at that point in time. First mover advantage isn't decisive, you have to actually be able to capitalize on it in a robust way.
This presupposes AGI or ASI are a real, reachable thing. My read is they might be, but not as LLMs. Until there's a fundamental rearchitecture, I'm AGI-agnostic and given that view, it doesn't seem rational to bet the house on it.
The non-doom scenarios include "utopia for all", and "power flows to investors, not citizens of whichever nation the winning model's corp. was registered in".
Independently, "oh look all the investors went bankrupt" can happen in both "doom" and "normal technology" timelines.
Yeah? And here I've been a happy Transkribus customer for some time now. If there are better models or interfaces out there for analyzing historical handwriting, I'll definitely take a look.
For anyone interested, I have an ocr pipeline running on rented GPUs, doing around 1000pages for 0.05-01 usd with around 0.8 seconds per page with full bounding boxes support for grounding.
If you’re interested you can find contact to me via this profile.
Can it produce accessible PDF files that will pass accessibility tests? Someone who can do that will make a killing laundering PDFs for academia: by April 26, every PDF, syllabus, and academic document needs to comply with WCAG 2.1 Level AA, which means structural tagging, alt text, and lots of other checklist items that AI could probably generate.
Accuracy is truly what people die for in the OCR game. Price isn't the primary function here.. it's an equation of price, accuracy, speed, and in mayn cases regulation.
Tbf even with tesseract you already get shit ton of accuracy and you can probably do these 1000 pages for way less than 3.5€. For 3.5€ you can spin up a cloud instance with 8vCPU+32gb on gcloud for 11 hours (or 11 instances for an hour) which can do way more than 1000 pages per hour on tesseract. It takes you around 6 second per page +-4 seconds start/stop depending on what you are doing on that instance size without too much optimization (you can probably even run multiple processes on a single node)
Google documentai costs 1.5$ per 1000 which is probably better in quality and speed.
Tesseract is not a substitute for these models, which understand complex layouts and also extract bounding boxes for things like tables and pictures. They are also much better at making sense of cursive scripts.
I’ve been there, implementing a way to linearise text from a document with pages with 1, 2 or 3 columns, some of them in landscape is a nightmare. And that’s not even considering equations.
In the end it’s way easier to use a specialised model, trained by other people to do exactly what I need.
Most use cases dont need that kind of accuracy, just doesnt justify the 3-4usd range. I build for that exact case (tender documents, we’re processing north of 100k pages per day), it doesnt need to recognize scanned written text from 1930s, its usually pdf/docs/scanned printed pages.
The accuracy is great, bounding boxes are must have for proper grounding for building answers by LLMs. Tesseract was too slow and not enough in some cases (for example tables or images which we also recognize and describe)
If you're getting inaccurate results from OCR what's the purpose of even doing it? Inaccuracy of text of any kind seems like a completely obvious failure of the entire purpose of scanning text into a computer.
It depends on what you need. For example a while ago I scanned and OCR'ed a bunch of receipts to get a timeline of my salary. I only cared about the gross and net figures, and nothing else mattered. Tesseract's output had a bunch of errors and misdetections, but the main figures always came out OK, and a local LLM was able to pick them out from the noise every time.
There's a big gulf between "it's as if a human being had transcribed it and reconstructed the original document" and "so completely broken it can't be used for anything".
Accuracy can have different dimensions, depends on what you can tolerate and whether you can detect it to apply more powerful methods.
Imagine you have a cheap and 99% accurate ocr. The other 1% you can detect and apply more powerful (more accurate but slower and more expensive) ocr method.
What would you use? At scale these things add up.
I've been experimenting with using NuExtract this week on locally OCRing bank statements that don't have a predefined document structure. It's way better than Tesseract or a generic vision-enabled model. It runs great on a single RTX 4090 at the modest throughput I need.
Their hosted, API-based service is something like a third of the cost of this model.
How does this compare to Baidu Unlimited OCR. I've been very impressed with Baidu and it's essentially free to run on a decent computer, other than electricity costs.
I've got a scan from a book that I OCR with new releases. Ligatures, critical sigla, Fraktur letterforms, subscripts, superscripts, etc.
Nothing special about this model for overly-detailed work like mine.
It's been a while since I last tested (and discontinued my subscription), but the "pro" models from OpenAI dominate. Not surprising, given the price difference, but it would be nice if an OCR-specific model could perform better. It's worth mentioning that even the highest-end models do a pretty poor job with intricate text like mine.
I have been quite happy with Mistral OCR for the documents I needed to process (typeset, but old, with questionable scan quality, sometimes elaborate typesetting or, much worse, typewriter-and-handwriting approximations of it). I do not test every new model when they are released, but I did a review shortly after Mistral OCR 3 was released and it was a very good compromise: cheap, fast, and good results without further processing. I found generalist models to be way too much faf to get them to avoid unnecessary modifications to the text and report accurate bounding boxes for figures and tables.
That said, models have sometimes surprising weaknesses and a model could be terrible overall but magically work for one type of document.
I got the opposite experience very recently : tried to OCR a bunch of handwritten emails addresses with chatGPT and I had to make so many corrections that I gave up. Whereas Mistral nailed it on first pass.
Yet: how is pricing? Evaluating contents and routing appropriately isn't a new challenge in OCR, one of the oldest fields of applications in ML. Thus, how do the smaller open models perform in tandem with relatively pricy $/pg models & APIs? Your use case is remarkably rare relative to the volume and price sensitivity of enterprise data warehouse ops.
> the "pro" models from OpenAI dominate. Not surprising considering the price difference, but it would ne nice if an OCR-specific model could do better.
I haven't been impressed with any of Mistral's models. They obviously realized that they couldn't compete at the frontier so they decided to go for smaller focused models but even those have not been that good.
At this point I lost all hope for Europe playing any significant role in the AI race. If that’s a good or a bad thing I don’t know, but it seems to me like that’s the reality.
It's not a race. You don't get anything for winning.
How about "being able to align ASI somewhat to your values"?
True, but that isn't EU/US/China, it's OpenAI/Anthropic/Grok/ …/DeepMind (based in UK)/… DeepSeek
With a lot of Chinese nationals in American companies, and an American corporation owning DeepMind, and a lot of people very upset with all of them at the same time, this is very messy.
The only prize is control of the light cone.
? It absolutely is a race. Whether thats a positive thing or not is debatable but every lab is definitely in a race. What prize do you win? Imagine a world where only one country has AGI/ASI. Or a world where Europe only gets access to frontier models 6 months later. Far from ideal.
> What prize do you win? Imagine a world where only one country has AGI/ASI.
"Winning the race" doesn't give you that in any meaningful capacity. It gives you, at the absolute most, a temporary window where that's the case. See: nuclear weapons.
As if time isn't money? Everything is temporary... Getting somewhere first has immense value.
A reductive equation, economics isn't thermodynamics. Money is fictional and value is subjective and unstable. Within this context, being first to AGI means nothing if the second invention of it comes 2 months later and works an order of magnitude faster than what the first iteration had self-improved to at that point in time. First mover advantage isn't decisive, you have to actually be able to capitalize on it in a robust way.
This presupposes AGI or ASI are a real, reachable thing. My read is they might be, but not as LLMs. Until there's a fundamental rearchitecture, I'm AGI-agnostic and given that view, it doesn't seem rational to bet the house on it.
We'll see.
A race to the bottom.
It's red queen. You stay alive by winning, you lose everything by losing.
Not sure you get either outcome in either case.
Race dynamics increases p(doom) for everyone.
The non-doom scenarios include "utopia for all", and "power flows to investors, not citizens of whichever nation the winning model's corp. was registered in".
Independently, "oh look all the investors went bankrupt" can happen in both "doom" and "normal technology" timelines.
Unless you manage to build a god, and keep it under control... okay, we're all going to lose
Not being a rat in the rat race is the real win.
Yeah? And here I've been a happy Transkribus customer for some time now. If there are better models or interfaces out there for analyzing historical handwriting, I'll definitely take a look.
1000 Pages / 3.5€ this is expensive as hell. If this is not fastly superior than something like tesseract it is not worth it.
Agreed. Does the GTM team there really sit together like "oh yeah, that sounds reasonable" while being totally beyond typical market prices?
For anyone interested, I have an ocr pipeline running on rented GPUs, doing around 1000pages for 0.05-01 usd with around 0.8 seconds per page with full bounding boxes support for grounding.
If you’re interested you can find contact to me via this profile.
3.5 usd/1000 pages is just too expensive…
Can it produce accessible PDF files that will pass accessibility tests? Someone who can do that will make a killing laundering PDFs for academia: by April 26, every PDF, syllabus, and academic document needs to comply with WCAG 2.1 Level AA, which means structural tagging, alt text, and lots of other checklist items that AI could probably generate.
Accuracy is truly what people die for in the OCR game. Price isn't the primary function here.. it's an equation of price, accuracy, speed, and in mayn cases regulation.
Tbf even with tesseract you already get shit ton of accuracy and you can probably do these 1000 pages for way less than 3.5€. For 3.5€ you can spin up a cloud instance with 8vCPU+32gb on gcloud for 11 hours (or 11 instances for an hour) which can do way more than 1000 pages per hour on tesseract. It takes you around 6 second per page +-4 seconds start/stop depending on what you are doing on that instance size without too much optimization (you can probably even run multiple processes on a single node)
Google documentai costs 1.5$ per 1000 which is probably better in quality and speed.
Tesseract is not a substitute for these models, which understand complex layouts and also extract bounding boxes for things like tables and pictures. They are also much better at making sense of cursive scripts.
I’ve been there, implementing a way to linearise text from a document with pages with 1, 2 or 3 columns, some of them in landscape is a nightmare. And that’s not even considering equations.
In the end it’s way easier to use a specialised model, trained by other people to do exactly what I need.
Most use cases dont need that kind of accuracy, just doesnt justify the 3-4usd range. I build for that exact case (tender documents, we’re processing north of 100k pages per day), it doesnt need to recognize scanned written text from 1930s, its usually pdf/docs/scanned printed pages. The accuracy is great, bounding boxes are must have for proper grounding for building answers by LLMs. Tesseract was too slow and not enough in some cases (for example tables or images which we also recognize and describe)
If you're getting inaccurate results from OCR what's the purpose of even doing it? Inaccuracy of text of any kind seems like a completely obvious failure of the entire purpose of scanning text into a computer.
It depends on what you need. For example a while ago I scanned and OCR'ed a bunch of receipts to get a timeline of my salary. I only cared about the gross and net figures, and nothing else mattered. Tesseract's output had a bunch of errors and misdetections, but the main figures always came out OK, and a local LLM was able to pick them out from the noise every time.
There's a big gulf between "it's as if a human being had transcribed it and reconstructed the original document" and "so completely broken it can't be used for anything".
Accuracy can have different dimensions, depends on what you can tolerate and whether you can detect it to apply more powerful methods.
Imagine you have a cheap and 99% accurate ocr. The other 1% you can detect and apply more powerful (more accurate but slower and more expensive) ocr method. What would you use? At scale these things add up.
Is it European-hosted and fully outside of both CLOUD Act and CCP reach?
Because I'm assuming that's why they get to charge more for the right type of customer.
You can even run it on your desk if you want, a single gtx 4090 is enough. It can be fully air gapped.
You should put contact details in your profile :)
The chinese did it better, mistral is alive thanks to regulations.
Yeah, I'm not sending personal bills etc to china. No thanks
i.e. it's one AI company that's basically guaranteed to never fail since it has a market niche guaranteed by European companies and governments.
Which is also why their most recent model "Shieldstral" does nothing except monitor and moderate internet content.
After stuff like Chat Control I think they're obviously seeing a big demand for this kind of "internet safety" technology in Europe.
No it's the other way round : the Chinese do better thanks to regulations : massive amounts of money from Big tech and public money.
I've been a bit more careful about complaining about regulations broadly due to competitive advantage, e.g. ITAR
I won't comment on accuracy, but in internal benchmarks, Mistral OCR is significantly faster than comparable APIs.
Mistral is bumping the price of this thing every release. I think we're at 2x now?
I've been experimenting with using NuExtract this week on locally OCRing bank statements that don't have a predefined document structure. It's way better than Tesseract or a generic vision-enabled model. It runs great on a single RTX 4090 at the modest throughput I need.
Their hosted, API-based service is something like a third of the cost of this model.
How does this compare to Baidu Unlimited OCR. I've been very impressed with Baidu and it's essentially free to run on a decent computer, other than electricity costs.
Where do your documents go?
They go to the decent computer hosting the model, which can be yours if you pay the electricity costs
Whoever is paying all that for OCR is being scammed.
* scanned
How does this compare to 4?