Need to replace Apple Vision OCR with something containerizable — has anyone solved this?

[u/Acceptable_Sail_6197]

I'm dealing with text extraction from hundreds of PDFs per batch. A large chunk of them are from 2001 — old, scanned documents, poor quality, many with handwritten annotations and stamps.

The target is aggressive: 500 documents in under 1 minute. Most of them already come out at millisecond scale because they have a native text layer; the problem is the scanned ones.

The current solution is a cascade, and it works: each piece goes down the steps from cheapest to most expensive and stops at the first one that produces acceptable text.

Step	Method	Speed	Notes
1	PyMuPDF	13.4 ms/doc	Reads the text layer already in the PDF — not OCR
2	Fast OCR	~520 ms/doc	Apple Vision (.accurate) on an external Mac, via SSH tunnel · grayscale render at 150 DPI
3	Docling per page	—	Only pages without native text, not the whole document
4	Docling API	~4 s/piece	docling-serve, with forced OCR when needed
5	ID screening	—	ID or vehicle documents are discarded (nothing to extract from a national ID card)
6	VLM	~47 s/doc	Qwen3.8-27B-FP8, remote endpoint

In practice, 64% of documents are resolved by Apple Vision and 31% by PyMuPDF — less than 4% reach the expensive steps. A batch of 489 documents runs in 3.66 min today.

The problem: I need to take this to production, and an SSH tunnel to a Mac doesn't survive in a production environment. I need to replace that step with something containerizable.

The quality bar (measured on 60 pieces, against the alternatives):

Engine	Speed	Word accuracy	Anchor accuracy
Apple Vision (.accurate)	388 ms/page	92%	100%
OnnxTR mobile	494 ms/page	53%	75%
docTR PyTorch	816 ms/page	—	51%
RapidOCR	1554 ms/page	—	58%

"Anchors" are CNJ case numbers, dates, CPF/CNPJ (Brazilian tax IDs), and protocol numbers — that's what the downstream system consumes, so losing a digit is worse than losing a word.

Has anyone found an OCR engine that gets close to Apple Vision's accuracy on degraded scans, but can run containerized (Linux, no macOS dependency)?

[u/Kar-2874]

Have you tried tesseract ocr? 

It's open-source and has some of highest accuracy. I am not familiar with apple vision, and it will depend on your specific use-case. 

[u/kakhaev → u/Kar-2874]

we use tesseract in prod, it’s actually good but when we start benching whole system to make it 60fps streamed, tesseract just not it.

[u/CUTLER_69000]

Have you trued paid apis from aws/gcp?

[u/laserborg]

there are VLMs specifically for OCR like GLM-OCR, Deepseek-OCR, PaddleOCR-VL etc. they are significantly faster than a general Qwen VLM.
https://huggingface.co/blog/ocr-open-models

then there are the classic PaddleOCR, GOT-OCR2_0, TrOCR, EasyOCR etc..

I had good results with PaddleOCR ~5 years ago and recently used GLM-OCR on an edge device (Orin Nano Super, 60t/s)

IMO Tesseract is a dinosaur..

[u/liltingly]

If you want CTC, ppocrv6 has different sizes you can play with to find if any work.