Thank you for the suggestions. All of those look like servers for the Tensorflow model (i.e. take what I already have on my laptop, and put it in the cloud). R

Thank you for the suggestions. All of those look like servers for the Tensorflow model (i.e. take what I already have on my laptop, and put it in the cloud). Real life doesn't give me folders of training data to sort. It gives me photos and videos. Applying a systems engineering approach, I think I need to: 1. Break the picture down into thousands of small little squares. 2. Look for a character inside each picture. 3. If a character is found, add the character to the output text. That brings new questions. How big should each square tile be? What if the characters are not perfectly flat? How do I avoid getting duplicates from tiles that are next to each other? Whether the tile->text conversion is done with a simple classifier or "deep learning model", the bulk of the work is done using non-Tensorflow programming. I don't know about open-source projects I could build on and retrain using my data. So the most likely result is that I'll show off the fancy Tensorflow data as a "portfolio project" and archive it, never to be used in production.