metadata

title: Neochar
emoji: 🖼
colorFrom: purple
colorTo: red
sdk: gradio
sdk_version: 5.25.0
app_file: app.py
pinned: false
license: openrail
short_description: Unwritten Chinese Charecters in Style

What is this?

Generate New Characters by combining parts in creative ways. Write them in a controlled style.

Inspired by
- Lin Yutang's Ming-Kwai typewriter
- Wu Yue's Glyffuser

Why

Fun to generate valid but unseen characters. (Never in a dictionary, nor Unicode).
Implements Lin Yutang's ideas with generative AI/ML, without the mechanical marvel :-/ or limitations :-)
Extends a font to support new charsets, and beyond to non-existent chars.
Adds variation/diversity/personality to generated images. No boring duplicates from the same char.
Other Creative Uses

How to use this app

Combine components or radicals in the following way
Specify the 'Structure' and 'Components', in a Polish Notation fashion - Good for tree structures
- ⿰: 'LR' Left-Rigth
- ⿱: 'TB' Top-Bottom
- ⿸: 'TL' Top-Left
- ⿹: 'TR' Top-Right
- ⿺: 'BL' Bottom-Left
- ⿴: 'OI' Outer-Inner
- ⿻: 'OV' Overlap
- ⿲: 'LMR' Left-Middle-Right
- ⿳: 'TMB' Top-Middle-Bottom
- ⿵: 'BT' Bottom Open Enclosure
- ⿶: 'CT' Top Open Enclosure
- ⿷: 'RT' Right Open Enclosure
Select a 'Style' by clicking the sample images
Hit the 'Generate' button
Repeat

Usage Tips

Simple structures work best (⿰ ⿱ ⿴ etc.)
"Known radicals at seen positions" work best (釒on left better than right, but may also surprise you in a good way)
Noto font family (sans and serif) gives the best results, as there are many training examples
Cursive and handwritten styles usually give good results, as they are more tolerant
Fonts supporting less chars are challenging
- Current model was trained with 300k samples for only 20 epochs
- Training will continue if this app gets attention or likes
For dictionary chars, decompose first.
For a part hard to describe, or you don't care, use a wildcard '？' (full-width question mark, or does it matter?)
What to do when the results are not as expected
- Pick a different 'sytle' which may have trained the model better
- Try again with a different random seed. This will change the overall structure in an unpredictable way
- Try again with a different 'step' number. This will change the local details in a continuous way

Creative Uses

Turning a bug into a feature

When you see a funny result you didn't expect (5 or 3 dots while it should be 4), don't throw it away immediately.

Save the results to confuse/train OCR
3vade 3vil c3nsorship
Share in discussion. The input text/seed/step will reliably reproduce the result.

Future Features

Typewriter keyboard for hard-to-input radicals, filtered by pinyin prefix
Direct generation from a single char, auto decomposition