Live data from Hacker News

GLM-OCR – A multimodal OCR model for complex document understanding

github.com

81–82 of 82 posts

Re: GLM-OCR – A multimodal OCR model for complex document understanding

#81
post #73
post #51

Earlier quoted context omitted.

Documents that come from FOIA. So, some scanned, some not. Lots of forms and lots of hand writing to add info that the form format doesn't recognize. Lots of repeated documents, but lots of one-off documents that have high signal.

I'd be very curious what works well with FOIA historical documents that have been scanned by hand with redactions by markers & etc.

I like to use textual anchors for things like, "line starts with" or "line ends with" or "file ends with" and combining that with levenshtein distance with some normalization stuff (combining adjacent strings in various patterns to account for OCR wonkiness). Turns into building lists of anchors that can be built off of. Of all the things I've tried, including things like image hashing and such, it's been the most effective generalized "tool".

But also, I hold the strong philosophy that it's important to actually read the documents that are being scanned. In that way, OCR tends to be more of a procedural step than anything.

Really, it ultimately depends on your goals.

Re: GLM-OCR – A multimodal OCR model for complex document understanding

#82

Earlier quoted context omitted.

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.

That's actually amazing, and might give me a way to use all the cores I have lying around. 2s per page is an insane 600 pages per minute at 20 cores!

Please do open source it, even if you don't do much around it (worst case I can just spend a few million tokens trying to get opus 4.6 to get it to work)

Post reply on HN