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About this paper
Ryo Ishiyama, Shinnosuke Matsuo, Seiichi Uchida
Kyushu University researchers developed a computer-aided framework for simplifying multi-stroke characters by selectively removing strokes, using a deep neural network to quantify legibility. The method consistently preserves character recognition better than random removal, demonstrating that characters can maintain full legibility even after optimally losing a substantial number of strokes.
Key insights
- Optimal stroke removal consistently outperforms random removal in preserving computed legibility, indicating the importance of selective, non-greedy stroke identification.
- Characters exhibit varying 'removal tolerance' to stroke loss; characters with more initial strokes generally tolerate more removals, and unique or distinctive strokes are critical for maintaining identity.
- The optimal simplification process tends to preserve the character's global shape and structural balance by incrementally removing strokes from different regions rather than entire components.
Summary by alphaXiv.
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