ML / NN have been around for a while, but there are a few reasons Scribble is only possible now:
1) Although classifying MNIST digits is the "hello world" of ML, doing the same with notes is substantially more difficult. The algorithm has to figure out sentence structure, punctuation, paragraph breaks, lists, and tons of other features that are hard to train. This problem is still a major research topic academically.
2) As a corollary to (1), while OCR has been around for a while, handwriting OCR has never worked due to (1).
3) Computing power has never been so cheap, training the algorithm would have been very expensive before AWS / Azure / etc abstracted hardware and made it inexpensive
Do you need links or proofs if I tell you the sky is blue ?
OCR has been around for decades. This approach is only interesting if it improves OCR via NN. Would love to learn more about this process (the improvement, not the training...).
ML / NN have been around for a while, but there are a few reasons Scribble is only possible now:
1) Although classifying MNIST digits is the "hello world" of ML, doing the same with notes is substantially more difficult. The algorithm has to figure out sentence structure, punctuation, paragraph breaks, lists, and tons of other features that are hard to train. This problem is still a major research topic academically.
2) As a corollary to (1), while OCR has been around for a while, handwriting OCR has never worked due to (1).
3) Computing power has never been so cheap, training the algorithm would have been very expensive before AWS / Azure / etc abstracted hardware and made it inexpensive