ocrmac VS unstructured-inference

Nice post, OP! I was super impressed with the Apple's vision framework. I used it on a personal project involving the OCRing of tens of thousands of spreadsheet screenshots and ingesting them into a postgres database.

I used a combination of RHetTbull's vision.py (for the actual implementation) [1] + ocrmac (for experimentation) [2] and was pleasantly surprised by the performance on my i7 6700k hackintosh.

I wouldn't call myself a programmer but I can generally troubleshoot anything if given enough time, but it did cost time.

[1]: https://gist.github.com/RhetTbull/1c34fc07c95733642cffcd1ac5...

[2]: https://github.com/straussmaximilian/ocrmac

Tesseract is widely known to be "meh" at this point.

If you look at RAG frameworks as one example they'll typically use/support a variety of implementations. Tesseract is almost always supported but it's rarely ideal with projects like Unstructured[0] and DocTR[1] being preferred. By leveraging more-or-less SOTA vision models[2][3] they embarrass Tesseract.

I haven't compared them to the Apple Vision framework but they're absolutely better than Tesseract and potentially even Apple Vision.

[0] - https://github.com/Unstructured-IO/unstructured-inference

[1] - https://github.com/mindee/doctr

[2] - https://github.com/mindee/doctr#models-architectures

[3] - https://github.com/Unstructured-IO/unstructured-inference#mo...