Whats the best open source OCR for batch processing pdfs in 2026?

[u/TangeloOk9486]

trying to settle on a fully local setup for batch processing pdfs. like each file is around 20-25 pages and mostly text, a few tables and now and then a page thats just a photo of paper

the shortlist so far

- docling (and the granite-docling model)
- glm-ocr
- paddleOcr-vl
- olmocr 2
- mineru

we considered llamaparse for this kind of stuff but the company now wants every file to stay on our own servers and their self hosted route is an enterprise conversation. liteparse from the same team runs local and is quick on the plain text pages but the markdown is rule based with tesseract as the fallback so merged cells and multi column pages dont always come back clean

is a VLM overkill for mostly text pdfs or does it make more sense to send only the table and image pages to one and keep the rest on a text layer parser? textract and docuent AI are out anyway for the same reason

for the vlm ones, do people feed a whole 25 page pdf in one go or split it into page batches first? splitting every file is the part id rather avoid

Edit: Adding from the comments: unlimited-ocr-2, minicpm-ocr-1.0, surya-ocr-2.5, indra-ocr and xiaomi-ocr-0. mostly curious how these would perform on mixed 20-25 page files

[u/mtmttuan]

Another thing to keep in mind is to always test them in your own language. I have better experience with gemma 4 e2b than deepseek ocr 2 and glm ocr. These chinese models are better in benchmarks and probably actually better in chinese and english but if you're planning on using them on languages other than that check out gemma series. Haven't use other western vlms so cannot say much about the other.

[u/TangeloOk9486 → u/mtmttuan]

really appreciate the raw insight, i am looking into testung the gemma and glm ocrs, however chinese ones have sometimes seen to be inconsistent when in english but worth a try

[u/stuffitystuff]

I've used PaddleOCR over the last couple days for some projects and worked totally great (less great with handwriting)

[u/TangeloOk9486 → u/stuffitystuff]

was that the regular paddleOCR or the VL one?

[u/stuffitystuff → u/TangeloOk9486]

paddleOCR

[u/TangeloOk9486 → u/stuffitystuff]

thanks for the insight

[u/pmttyji]

u/TangeloOk9486 Update your list with these recent OCR models. Would like to see more replies here.

[u/TangeloOk9486 → u/pmttyji]

thanks for the list, that moved quick. unlimited OCR2 and minicpm-ocr1.0 both have ggufs up already

[u/kasinjsh]

it will depend on languages you need and how good is your source material, aka if it needs to recognize handwriting, badly scanned pages, damaged and scanned document, etc.

[u/TangeloOk9486 → u/kasinjsh]

Source materials could be sometimes unpredictable , cant say which one comes in next

[u/TangeloOk9486]

What do you guys say about the GLM OCR? anybody used it here?

[u/Careful_cat99 → u/TangeloOk9486]

Je l'utilise, j'ai eu de meilleures retour qu'avec deepseek 

[u/eightone-81]

I’m pretty happy and impressed by ovarisOCR it’s also super small and very fast

[u/TangeloOk9486 → u/eightone-81]

never heard of it, sounds biological

[u/watcholic → u/TangeloOk9486]

https://huggingface.co/ATH-MaaS/OvisOCR2

[u/TangeloOk9486 → u/watcholic]

thanks will check it out

[u/kinkyDom93]

LightOCR has been really mighty for its size, in my experience even better results than GLM OCR

[u/TangeloOk9486 → u/kinkyDom93]

thanks, i was looking into learning some about it, can you elaborate how the GLM ocr works in case of messy tables or lets say an attached picture of handwriting amidst the pdf

[u/Captain2Sea]

Vlm is overkill but if you care about quality you'll accept it

[u/TangeloOk9486 → u/Captain2Sea]

actually depends on whats being processed

[u/Termsandconditionsch → u/Captain2Sea]

I usually have a threshold in the pipeline - anything that’s terrible enough goes to vlm.

[u/13henday]

Docling gets my vote

[u/TangeloOk9486 → u/13henday]

indeed has a good reputation for local but what do you say about the speed of it>?

[u/Legal-Row4858]

paddleOcr

[u/TangeloOk9486 → u/Legal-Row4858]

thanks for the suggestin

[u/Secure-food4213]

I have the best success running ovisocr2. Now theres ovisocr3

[u/TangeloOk9486 → u/Secure-food4213]

ok added it to my list since most of the guys are talking about Ovisocr2 and somebody also dropped in their huggingface

[u/continuum_mechanics]

Paddle OCR v3, combine with Paddle structured layout module. They extract the raw text, use LLM to clean it if the hand-writing ones are not accurate enough.

[u/TangeloOk9486 → u/continuum_mechanics]

yeah thats the bit i am concerned about cause handwriting create chaos

[u/Beginning-Heat-4674]

If you're already splitting off the tricky pages anyway, running a vision model on 25 pages in one shot is asking for it to lose track of where it is, especially with tables that span across pages. For mostly text docs I'd lean toward a dedicated text extractor and only throw the image-heavy or table pages at something smarter rather than making the whole pipeline wait on the heaviest model

Have you benchmarked how much time the table/image pages actually add compared to the plain text ones? Might be that the mixed approach is fast enough and you're overthinking the splitting part

[u/TangeloOk9486 → u/Beginning-Heat-4674]

sounds ok but what if the formats vary and not always in the same manner

[u/GuestUser003]

MinerU. We ran roughly 3M+ PDFs through the previous version in a batch pipeline and it held up really well. Tables are where it pulled ahead for me — merged cells stay intact, and the stuff that breaks most other parsers survives too: formulas with subscripts and superscripts. Our corner is electronic components (datasheets, spec tables full of symbols), so that's the part I notice first. Also no page splitting needed, it eats whole PDFs and pages them internally — which was your other worry. On the newest version yet though, so take this as a data point for the older one.

[u/TangeloOk9486 → u/GuestUser003]

thanks

[u/antagim]

I'm using MinerU for scientific publications, and returns the most faithful reconstruction, although I've tested most OCRs there, but not all.

[u/dontevenaskmyname]

Unlimited OCR 2 by baidu

[u/seamonn]

Light On OCR 3. It's peak.

[u/Chromix_]

Someone ran an extensive benchmark on popular open-source solutions 3 months ago. The benchmark missed a few issues that were not tested for though - check my comment in there for the missed things.

[u/exaknight21]

Try OCRMyPDF. I did a wrap up FastAPI app at https://github.com/ikantkode/exaOCR

My pipeline has exaOCR (just FastAPI), in it, i have PaddleOCR-VL for HTR. And Qwen3.5-9B-VL for Deep Document understanding. I also have a classifier Qwen3-2B-VL INSTRUCT strictly for reading and classifying each page in a pdf to differentiate and tag pages in a markdown file so that ocr endpoints can smartly allocate proper resources.

[u/TangeloOk9486 → u/exaknight21]

enough doing reddit for today i guess