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millimacro

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1 points·by millimacro·il y a 9 mois·0 comments

[untitled]

1 points·by millimacro·il y a 10 mois·0 comments

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1 points·by millimacro·l’année dernière·0 comments

Edinburgh (UK) has 2,500 bus stops and they are typically 323M apart

lellep.xyz
2 points·by millimacro·l’année dernière·0 comments

1,600 days of a failed hobby data science project

lellep.xyz
158 points·by millimacro·il y a 2 ans·70 comments

Edinburgh Pothole Logger: A FOSS App to Log Potholes in Edinburgh, UK

lellep.xyz
2 points·by millimacro·il y a 2 ans·0 comments

Handwritten Text Recognition for Xournal++ Using Deep Learning

github.com
37 points·by millimacro·il y a 2 ans·19 comments

Pi Stack: FOSS Stackable Raspberry Pi Housing

lellep.xyz
1 points·by millimacro·il y a 2 ans·0 comments

comments

millimacro
·il y a 6 mois·discuss
This is very cool! I wonder - does this also work reasonably fast on older hardware?
millimacro
·il y a 2 ans·discuss
Cheers everyone for your lovely engagement!! <3
millimacro
·il y a 2 ans·discuss
I was wondering, how do you capture handwritten notes using Xournal and Xournal++? Using some sort of tablet maybe?

Great to hear that you found out about Xournal++! It's really the power of open-source that you own your own handwritten notes as you are not locked in.
millimacro
·il y a 2 ans·discuss
"HTR is an oasis in this global desert of manual digitization" - hach, this is such a great phrasing! <3

Xournal++ is a great project that features a bunch of really great developers who dedicate a lot of time to it.

I am not much involved in the Xournal++ development itself but then try to utilise my machine learning skills to build an HTR system for Xournal++ in the form of a plugin.

Cheers! :-)
millimacro
·il y a 2 ans·discuss
Yes, you're right, stroke-order-aware HWR are hard to find. One reason for that is the lack of good datasets for machine learning model training!

As such, my stroke-order-aware attempt over at https://github.com/PellelNitram/OnlineHTR/ uses a dataset from 2000 with around 12,000 samples. Contrary, the internal Google dataset is reported to feature around 16,000,000 samples :-D.
millimacro
·il y a 2 ans·discuss
This is a great observation!

Currently, the machine learning model only supports offline HTR (i.e. using images) but online HTR (i.e. using pen time series data) is in the making, see here:

https://github.com/PellelNitram/OnlineHTR/
millimacro
·il y a 2 ans·discuss
Yes, that's totally correct! The current version of the plugin supports only so called "offline" HTR, which operates on images. This is ultimately determined by the underlying machine learning model.

I have developed another model however (based on a somewhat recent Google paper by Carbune et al. 2020), that operates on pen dynamics and thereby implements online HTR, see here:

https://github.com/PellelNitram/OnlineHTR

This model is open-source as well and will be part of the HTR system for Xournal++ in the future. Feel free to give it a try yourself locally.

One question that has been bothering me a long time and prevented online HTR so far for me is how to find text on a page in temporal domain (i.e. in online domain and not offline domain). If you have any ideas on that, please do let me know as I would greatly appreciate that! One possible way is a transformer model - but again that feels a bit overkill and introduces a context length.
millimacro
·il y a 2 ans·discuss
I totally agree with you!

The HTR feature in Xournal++ does not require you to scan anything though. You just write handwritten notes in Xournal++ as always and upon saving with the plugin, the resulting PDF is searchable (the handwritten texts at least). So there's no scanning or retyping involved :-).
millimacro
·il y a 2 ans·discuss
Yes, that's what I think as well!
millimacro
·il y a 2 ans·discuss
Oh that's so exciting to hear! Please do let me know if you need help when trying to use the plugin! I am happy to help you until the plugin works for you.
millimacro
·il y a 2 ans·discuss
I decompose my answer in three prime time metrics below and obtain that the plugin is at 6.4/10.

These are the decomposed prime time metrics:

1. The plugin itself is at 10/10; e.g. I use it in production myself.

2. The prediction quality is at, say, 6/10. I am actively working on this.

3. The installation process is 3/10. That's because I have only tested it using my machine setup, for which it actually works fine. To improve here, I'd love to get a bit of user feedback to improve the installation process.

Cheers!