ocrmac

The PP-DocLayoutV3 [1] bounding boxes are pretty good in my experience, if you want boxes around individual document headings or paragraphs. If you want boxes around individual words, similar to what's shown in the Interfaze screen shot [2], Apple has a LiveText "token" model that's proprietary but free/bundled with macOS and iOS. There are easy to use Python bindings here: https://github.com/straussmaximilian/ocrmac I presume that some otherwise-great OCR models (like Chandra) have terrible bounding boxes because generating good bounding boxes just wasn't a training priority. A lot of people are using OCR models to bulk-process documents without a lot of care for how the layout is preserved. It matters a lot if (e.g.) you want to be able to update and re-print old documents, but it doesn't matter if you are just transcribing whole docume

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 tried other OCR CPU methods (since macOS and Nvidia still don't play nice together) such as Tesseract but found the output to be incorrect too often. The vision framework was not only the highest quality output I had seen, but it also used the least amount of compute. It was fairly unstable, but I can chalk that up to user error w/ my implementation. 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 give