21–30 of 64 posts
Cool idea. Could see this being useful with something like Evernote. It's got a great note capture feature as well but doesn't covert to text you can't edit.
Agreed re: Evernote. I actually really like that feature, because it makes handwritten notes searchable but found the same problem you identified with the lack of transcription.
My hope is we can integrate with players like Evernote / OneNote who already do a great job at centralizing notes.
Now if only someone would release designs for an affordable, reliable, non-destructive robot to do the physical data collection... My backlog of notes is way to big to stand around snapping cell phone photos at all of it manually.
I've been keeping a physical dev diary and have been trying to keep up with transcribing it to digital, but would love any shortcuts to that process as I'm pretty bad about keeping up with it. My handwriting is kind of messy but I'm eager see how well your algorithms can handle it. It doesn't have to be perfect anyway, as I don't mind going in and cleaning up afterwards. Should still save me some time and some typing…
It's currently not perfect but handles a surprising amount of bizarre handwriting styles (cursive / messy notes). Looking forward to hearing your thoughts as we onboard to the beta,
this kinda thing was well studied and developed nearly 30 years ago already..... Deep leaning, ML are all hypes.
Without any links or proof, this is a low value comment.
Does the processing happen locally or is there some cloud processing going on? Kind of worried about the privacy implications of that.
It currently happens in the cloud (purely a software design decision), but we could probably do it locally as well since the NN is already trained.
We were thinking of having similar TOS as normal note editing software (Evernote, Dropbox Paper) to mitigate security concerns. What do you think?
Looks nice. Any chance for an api so we can send jpg and get back a document?
We don't have a developer facing API at the moment but it's in the roadmap. Once our algorithm is accurate enough that it "just works" in an enterprise setting, we may open up an API so developers can build applications for their businesses.
This looks cool but I wish it showed examples of different kinds of handwriting being detected. Cursive? European numbers vs American numbers (the 1 especially)? Doctor (or other badly rendered hand writing)? Seems like this technology isn't really all that useful if it doesn't work on various inputs, especially 'unclean' or 'sloppy' inputs.
>European numbers vs American numbers (the 1 especially)? Honestly I think 9 will be the bigger issue, to my eyes the European version is near identical to lower-case G.
Care to share some details on your technology? On whose handwriting was this trained, did you use any public datasets for this? And of course, how well will this perform on writing styles it hasn't seen before?
Scribble currently only supports English, so it does poorly with other languages, but is pretty robust to poor handwriting in English (such as my own).
It gets about 85% of my handwriting correct (my handwriting is abysmal), so there's definitely room for improvement.