https://zapier.com/blog/best-text-dictation-software/#window...
(Haven't actually tried Otter, but it gets a LOT of good reviews.)
Any techie will desperately try to come up with a tech solution to this problem.
A few months of development later, you might have something that yields trustworthy output.
But 16 hours? No tech solution will be done faster than that.
Don't build a factory for a one-off.
True
> Don't build a factory for a one-off.
One thing on my wishlist is that I end up with a way to instantly transcribe my notes.
Many of the implementations are clunky in my opinion, but this exists as a feature in many note taking tablet apps.
If you are willing to use special paper, there is existing Neo Smartpen ( https://shop.neosmartpen.com/ )
Both will force use to us D1 ballpoint pen cartridges, so no suggestions in you must write with favorite fountain pen, or are a Hi-Tec-C only pen lifestyle.
Adobe also has the whole scan thing, and apple can — in some cases — correctly transcribe characters from images.
I really doubt it can handle handwriting
Eventually, they’ll learn and speed will go up, but with this amount of work, work will be finished before they make up for the learning curve.
But overall, if I were suggest an ideal process: 1) transcribe notes w/ Whisper, 2) play back the media in VLC with the transcripts and correct the errors. T = 16 hours of proofing/correction + ~8 hours of headless transcription of *.wav before hand.
I’d be tempted to at least try breaking down the notes into one line long images (about a sentence) each and give it ago with Gemini. I haven’t tested their ocr, but even if it has errors, I bet you could just ask Gemini again to best fix the sentence.
Maybe other people can use the software, so it's not a one-off?
Documented here: https://notes.dsebastien.net/30+Areas/33+Permanent+notes/33....
I can recommend https://www.videototextai.com/ for transcribing huge amounts of audio. (Disclaimer, I am the founder of VideoToTextAI)
And, doh, took a minute, and realized I'm a dunce.* And now there's at least 3 I can think of off the top of my head, not to mention local.
Training a handwriting recognition AI is universally accessible. What a time to be alive.
* If you're dense like me: they're not saying "any AI" as "any handwriting recognition machine learning model you build from that dataset". They're saying as AI as any multimodal LLM, it'll do in context learning on what you upload.
note: I have no relation to MacWhisper, just a happy customer.
* after STT, there is objectively less info in the storage format
* OP cannot take advantage of rapidly advancing OCR tech on the storage
* inevitably OP might end up saving the originals “just in case”- rendering this entire process useless
In the days before good software transcription I saved a ton of time I grad school by splitting up interviews and using mechanical Turk or up work ( can’t remember which one, to transcribe 1 minute snippets, and then took another pass)
If a note's a minute, 1000 notes are around 16 hours of reading. Scale time needed depending on if it takes less or more than a minute to read. Add a note reference to the start of each recording, like a zettelkasten, so the scanned file, recording and text cross-reference.
If assessing other solutions, that's at least an upper bound on the cost of any other solution.