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--- |
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title: Neochar |
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emoji: 🖼 |
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colorFrom: purple |
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colorTo: red |
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sdk: gradio |
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sdk_version: 5.25.0 |
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app_file: app.py |
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pinned: false |
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license: openrail |
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short_description: Unwritten Chinese Charecters in Style |
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--- |
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# What is this? |
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Generate New Characters by combining parts in creative ways. Write them in a controlled style. |
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- Inspired by |
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- Lin Yutang's [Ming-Kwai typewriter](https://en.wikipedia.org/wiki/Chinese_typewriter#MingKwai_design) |
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- Wu Yue's [Glyffuser](https://yue-here.com/posts/glyffuser/) |
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# Why |
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- Fun to generate valid but unseen characters. (Never in a dictionary, nor Unicode). |
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- Implements Lin Yutang's ideas with generative AI/ML, without the mechanical marvel :-/ or limitations :-) |
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- Extends a font to support new charsets, and beyond to non-existent chars. |
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- Adds variation/diversity/personality to generated images. No boring duplicates from the same char. |
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- Other [Creative Uses](#creative-uses) |
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# How to use this app |
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- Combine components or radicals in the following way |
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- Specify the 'Structure' and 'Components', in a [Polish Notation](https://en.wikipedia.org/wiki/Polish_notation) fashion - Good for tree structures |
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- ⿰: 'LR' Left-Rigth |
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- ⿱: 'TB' Top-Bottom |
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- ⿸: 'TL' Top-Left |
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- ⿹: 'TR' Top-Right |
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- ⿺: 'BL' Bottom-Left |
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- ⿴: 'OI' Outer-Inner |
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- ⿻: 'OV' Overlap |
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- ⿲: 'LMR' Left-Middle-Right |
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- ⿳: 'TMB' Top-Middle-Bottom |
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- ⿵: 'BT' Bottom Open Enclosure |
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- ⿶: 'CT' Top Open Enclosure |
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- ⿷: 'RT' Right Open Enclosure |
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- Select a 'Style' by clicking the sample images |
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- Hit the 'Generate' button |
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- Repeat |
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# Usage Tips |
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- Simple structures work best (⿰ ⿱ ⿴ etc.) |
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- "Known radicals at seen positions" work best (釒on left better than right, but may also surprise you in a good way) |
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- Noto font family (sans and serif) gives the best results, as there are many training examples |
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- Cursive and handwritten styles usually give good results, as they are more tolerant |
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- Fonts supporting less chars are challenging |
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- Current model was trained with 300k samples for only 20 epochs |
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- Training will continue if this app gets attention or likes |
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- For dictionary chars, [decompose](https://github.com/cburgmer/cjklib/blob/master/cjklib/data/characterdecomposition.csv) first. |
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- For a part hard to describe, or you don't care, use a wildcard '？' (full-width question mark, or does it matter?) |
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- What to do when the results are not as expected |
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- Pick a different 'sytle' which may have trained the model better |
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- Try again with a different random seed. This will change the overall structure in an unpredictable way |
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- Try again with a different 'step' number. This will change the local details in a continuous way |
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# Creative Uses |
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## Turning a bug into a feature |
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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. |
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- Save the results to confuse/train OCR |
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- 3vade 3vil c3nsorship |
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- Share in discussion. The input text/seed/step will reliably reproduce the result. |
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# Future Features |
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- Typewriter keyboard for hard-to-input radicals, filtered by pinyin prefix |
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- Direct generation from a single char, auto decomposition |
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