[dangledangle]

Tesseract is the most ideal OCRA SDK for reading simple bw documents. If you aim to read “text In the wild” or scene text then Firebase offers a much better alternative. I had a lot of hope for Tesseract 4.0 which is supposed to be based on NNs but it’s so far performed just marginally better than 3.0

[jonatron]

For “text In the wild” or scene text, the last time I checked, EasyOCR and PaddleOCR were both good.

[danShumway]

I expected these to still be pretty low quality, but surprisingly some quick tests shows that EasyOCR seems to be doing relatively decently at pulling text out of smartphone pics of documents.

Thanks for sharing these -- it's maybe just my very bad searching skills but I had been trying to set some stuff up with Tesseract and had come to the conclusion that I just couldn't use it for document photos and would either need to abandon that effort and buy a faster scanner, or hook into some proprietary service like Google/Apple.

Both of these look really promising, so now I'm excited again about the potential of setting up a fast Open Source way to digitize my documents.

[elpakal]

Just IMHO Apple's Vision framework has been great too, and very easy to get started

[dangledangle]

Vision's rectangle detection or document scanner has worked well for us but in comparison to what Google's MLKit OCR offers it pales in comparison. MLKit OCR also does language detection + more languages out of the box.

EasyOCR is definitely interesting and something that's worked well for us at a prototyping level.

[cphoover]

This was my experience too... I tried to us tessaract for mobile app that scanned food labels in realtime using the camera video feed, I found that google's ML library text recognition was much faster, and reliable.

[anothernewdude]

And what are you supposed to use if you're not doing it on android and can therefore use firebase?

[cersa8]

It's part of the Cloud Vision API which supports (g)RPC and REST. Used it in a trading bot to detect if a tweet image (from Elon Musk) contained any mention of the text Doge or Dogecoin, or even a real dog.