Disclaimer: I'm an AI novice relative to many here. FWIW last wknd I spent a couple hours setting up self-hosted n8n with ollama and gemma3:4b [EDIT: not Qwen-3.5], using PDF content extraction for my PoC. 100% local workflow, no runtime dependency on cloud providers. I doubt it'd scale very well (macbook air m4, measly 16GB RAM), but it works as intended.
If you have a basic ARM MacBook, GLM-OCR is the best single model I have found for OCR with good table extraction/formatting. It's a compact 0.9b parameter model, so it'll run on systems with only 8 GB of RAM.
Then you can run a single command to process your PDF:
glmocr parse example.pdf
Loading images: example.pdf
Found 1 file(s)
Starting Pipeline...
Pipeline started!
GLM-OCR initialized in self-hosted mode
Using Pipeline (enable_layout=true)...
=== Parsing: example.pdf (1/1) ===
My test document contains scanned pages from a law textbook. It's two columns of text with a lot of footnotes. It took 60 seconds to process 5 pages on a MBP with M4 Max chip.
After it's done, you'll have a directory output/example/ that contains .md and .json files. The .md file will contain a markdown rendition of the complete document. The .json file will contain individual labeled regions from the document along with their transcriptions. If you get all the JSON objects with
"label": "table"
from the JSON file, you can get an HTML-formatted table from each "content" section of these objects.
It might still be inaccurate -- I don't know how challenging your original tables are -- but it shouldn't be terribly slow. The tables it produced for me were good.
I have also built more complex work flows that use a mixture of OCR-specialized models and general purpose VLM models like Qwen 3.5, along with software to coordinate and reconcile operations, but GLM-OCR by itself is the best first thing to try locally.
I also get connection timeouts on larger documents, but it automatically retries and completes. All the pages are processed when I'm done. However, I'm using the Python client SDK for larger documents rather than the basic glmocr command line tool. I'm not sure if that makes a difference.
Cool! For GLM-OCR, do you use "Option 2: Self-host with vLLM / SGLang" and in that case, am I correct that there is no internet connection involved and hence connection timeouts would be avoided entirely?
When you self-host, there's still a client/server relationship between your self-hosted inference server and the client that manages the processing of individual pages. You can get timeouts depending on the configured timeouts, the speed of your inference server, and the complexity of the pages you're processing. But you can let the client retry and/or raise the initial timeout limit if you keep running into timeouts.
That said, this is already a small and fast model when hosted via MLX on macOS. If you run the inference server with a recent NVidia GPU and vLLM on Linux it should be significantly faster. The big advantage with vLLM for OCR models is its continuous batching capability. Using other OCR models that I couldn't self-host on macOS, like DeepSeek 2 OCR or Chandra 2, vLLM gave dramatic throughput improvements on big documents via continuous batching if I process 8-10 pages at a time. This is with a single 4090 GPU.
