This is a great example of the AI effect where people would call something AI when it was a daunting research problem but give it another label once it’s working:
https://en.wikipedia.org/wiki/AI_effect
In this case, Apple’s modern OCR is a complex neural network system which I think most people would class as an AI tool. It’s notably better than traditional approaches which were optimized for business documents.
I'm not sure I agree, primarily because "better" is subjective.
A pipeline with template matching is extremely effective at extracting fixed form text in a standard layout, such as telephone numbers or credit cards, and computationally cheap as well.
But I presume a drop-in black box model which isn't bound to a low computational budget, can output plenty of false negatives and false positives, and can run on a single pipeline might be preferable at least from a product management point of view.
Also, neural networks looks good on resume while template matching doesn't. Just like statician/image analyst looks lukewarm but AI engineer looks superb.
Isn’t Tesseract also neural network-based?