Technically not OCR, but HTR (hand-written text/transcript recognition) is still difficult.
LLMs have increased accuracy, but their mistakes are very hard to identify because they just 'hallucinate' text they cannot digitize.
E.g.
Most current benchmarks have a scoring scheme of
1 - Correct Answer 0 - No answer or incorrect answer
But what they need is something more like
1 - Correct Answer 0.25 - No answer 0 - Incorrect answer
You need benchmarks (particularly those used in training) to incentivize the models to acknowledge when they're uncertain.
I don't have a use case of 100s or 1000s of hand-written notes have to be transcribed. I have only done this with whiteboard discussion snapshots and it has worked really well.