This might be a niche question, but does glm-ocr (or other libraries) have the ability to extract/interpret QR code data?
GLM-OCR – A multimodal OCR model for complex document understanding
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Re: GLM-OCR – A multimodal OCR model for complex document understanding
#62Earlier quoted context omitted.
Chrome ships a local OCR model for text extraction from PDFs which is better than any of the VLM or open source OCR models i've tried. I had a few hundred gigs of old newspaper scans and after trying all the other options I ended up building a wrapper around the DLL it uses to get the text and bboxes. Performance and accuracy on another level compared to tesseract, and while VLM models sometimes produced good results…
Surprisingly, I have a few hundred gigs of old newspaper scans so am very curious. How fast was it per page? Do you recall if it's CPU or GPU based? TY!
It is CPU-based. Somewhere between 1 to 2 seconds per page on a single core. I ran 20 instances of it in parallel to utilize 20 CPU cores so the avg time came down nicely.
Re: GLM-OCR – A multimodal OCR model for complex document understanding
#63Is it possible for such a small model to outperform gemini 3 or is this a case of benchmarks not showing the reality? I would love to be hopeful, but so far an open source model was never better than a closed one even when benchmarks were showing that.
Off the top of my head: for a lot of OCR tasks, it’s kind of worse for the model to be smart. I don’t want my OCR to make stuff up or answer questions — I want to to recognize what is actually on the page.
Sometimes what is on the page is ambiguous. Imagine a scan where the dot over the i is missing in a word like "this". What's on the page is "thls" but to transcribe it that way would be an error outside of forensic contexts.
I am reminded it's basically impossible to read cursive writing in a language you don't know even if it's the same alphabet.
Re: GLM-OCR – A multimodal OCR model for complex document understanding
#64What's the current SOTA for Japanese and Korean OCR? BalloonsTranslator has a great workflow but the models are pretty old.
Re: GLM-OCR – A multimodal OCR model for complex document understanding
#65Text me back when there's a working PDF to EPUB conversion tool. I've been waiting (and searching for one) long enough. :D EDIT: https://github.com/overcuriousity/pdf2epub looks interesting.
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Re: GLM-OCR – A multimodal OCR model for complex document understanding
#66This might be a niche question, but does glm-ocr (or other libraries) have the ability to extract/interpret QR code data?
zbar, pyzbar, OpenCV + QRCodeDetector, Gemini + Vision AI, Claude + Vision
Re: GLM-OCR – A multimodal OCR model for complex document understanding
#67There was so many OCR models released in the past few months, all VLM models and yet none of them handle Korean well. Every time I try with a random screenshot (not a A4 document) they just fail at a "simple" task. And funnily enough Qwen3 8B VL is the best model that usually get it right (although I couldn't get the bbox quite well). Even more funny, whatever is running on an iphone locally on cpu is insanely good,…
Chrome ships a local OCR model for text extraction from PDFs which is better than any of the VLM or open source OCR models i've tried. I had a few hundred gigs of old newspaper scans and after trying all the other options I ended up building a wrapper around the DLL it uses to get the text and bboxes. Performance and accuracy on another level compared to tesseract, and while VLM models sometimes produced good results…
Is there a chance you'll open source the wrapper after all? It would help a lot of people like me. No pressure though, but now I really want to try it to OCR a bunch of Japanese scans I have lying around. Unfortunately, finding a good OCR for Japanese scans is still a huge problem in 2026.
Re: GLM-OCR – A multimodal OCR model for complex document understanding
#68> Option 1: Zhipu MaaS API (Recommended for Quick Start)
> Use the hosted cloud API – no GPU needed.
...
> Option 2: Self-host with vLLM / SGLang
So, first off, this looks really cool and, given I'm looking for OCR at the moment, I'm pretty interested in this and other OCR models.
With that said, the README implies that option 2 requires a GPU. That's fine but it would be incredibly helpful if the README were explicit about requirements, and especially the amount of memory it needs.
EDIT: Looking at the links under option 3, the docs for macOS setup suggest 8GB of unified memory is enough to run the model, which is pretty modest, so I'd imagine Option 2 is similar. Ollama also offers a CPU only option (no idea how that will perform - not amazingly, I'm guessing), but that would suggest to me that if your volume requirements are low and you can't shell out for or source a beefy enough GPU and don't want to pay the sometimes exhorbitant hire costs, you should be able to punt it on to a machine with enough memory to run the model without too much difficulty.
Re: GLM-OCR – A multimodal OCR model for complex document understanding
#69Earlier quoted context omitted.
Deciphering fax messages? What is this, the 90s?
Fax is still hard to hack, so some organizations have kept it alive for security.
I think the most useful thing about faxes, security-wise, is that in their basic form they require zero digital storage of the image being sent. The only record on either side of the transmission is a piece of paper.*
Contrast that with email, which is store-and-forward by design, and now you have to put in effort to ensure both the sending and receiving email providers delete the message in a timely manner.
* obviously you can add store-and-forward behavior to either fax machine, but it's not the default.
Re: GLM-OCR – A multimodal OCR model for complex document understanding
#70Earlier quoted context omitted.
How do these compare to something like Tesseract? I remember that one clearing the scoreboard for many years, and usually it's the one I grab for OCR needs due to its reputation.
Tesseract v4 when it was released was exceptionally good and blew everything out of the water. Have used it to OCR millions of pages. Tbh, I miss the simplicity of tesseract. The new models are similarly better compared to tesseract v4. But what I'll say is that don't expect new models to be a panacea for your OCR problems. The edge case problems that you might be trying to solve (like, identifying anchor points, or…
I used Tesseract v3 back in the day in combination with some custom layout parsing code. It ended up working quite well. When looking at many of the models coming out today the lack of accuracy scares me.