zai-org/GLM-OCR

If you have a basic ARM MacBook, GLM-OCR is the best single model I have found for OCR over documents 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. https://github.com/zai-org/GLM-OCR Use the mlx-vlm back end: https://github.com/zai-org/GLM-OCR/blob/main/examples/mlx-deploy/README.md 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 tex…

You can still be excited! Recently, GLM-OCR was released, which is a relatively small OCR model (2.5GB unquantized) that can run on CPU with good quality. I've been using it for digitizing various hand-written notes all my shopping receipts this week. https://github.com/zai-org/GLM-OCR (Shameless plug: I also maintain a simplified version of GLM-OCR without dependency on the transformers library, which makes it much easier to install: https://github.com/99991/Simple-GLM-OCR/ )

I tested many open-source and hosted OCR models and Datalab Chandra was the most accurate. It can parse complex layouts, tables, handwriting, and formulas at a fraction of the cost of Claude/Gemini. Local: https://github.com/datalab-to/chandra Hosted: https://www.datalab.to Another decent option is GLM OCR. It's slightly less accurate but faster and cheaper. Local: https://github.com/zai-org/GLM-OCR Hosted: https://docs.z.ai/guides/vlm/glm-ocr Other models such as PaddleOCR, dots.ocr and DeepSeek OCR performed significantly worse.