> Since OCR is basically "hello world" for tensorflow, I don't understand why these incumbents haven't been wiped off the map.
Do you have an example of anyone working on that? I think those packages have a lot of inertia because you can buy a product with an API, documentation, etc. Something which says “Learn TensorFlow & enough AI to be dangerous” is going to be hard to get in the door at many large organizations so this might be an area where a solid open-source project could have a significant impact.
The parent post is not implying that companies adopt Tensorflow themselves and solve this problem. The post is implying that a competitor come in to the market using the newer, better technology, and replace them with a better product (with an API, documentation, etc).
This issue here is that industries become stagnant when it appears difficult/costly to introduce a new product in that market. You usually need significant a technical and/or social change to occur before incumbents can be displaced. In this case, it was a technical change.
The other part of the issue is that investors want "0 to 1" ideas to invest in, and founders often chase those ideas. In reality, we need more "1.0 to 1.5" products, especially in crowded markets.
I think your last point is correct: there's a huge gap between the kind of “hello world” demo the original poster was talking about and a product-level tool which has reasonable training data and performance across a non-trivial range of documents.
I think that getting there is a lot more work than OP anticipated.
Do you have an example of anyone working on that? I think those packages have a lot of inertia because you can buy a product with an API, documentation, etc. Something which says “Learn TensorFlow & enough AI to be dangerous” is going to be hard to get in the door at many large organizations so this might be an area where a solid open-source project could have a significant impact.