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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Online-Zugang: | https://arxiv.org/abs/2506.23106 |
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| _version_ | 1866912456032387072 |
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| author | Ishiyama, Ryo Matsuo, Shinnosuke Uchida, Seiichi |
| author_facet | Ishiyama, Ryo Matsuo, Shinnosuke Uchida, Seiichi |
| contents | Multi-stroke characters in scripts such as Chinese and Japanese can be highly complex, posing significant challenges for both native speakers and, especially, non-native learners. If these characters can be simplified without degrading their legibility, it could reduce learning barriers for non-native speakers, facilitate simpler and legible font designs, and contribute to efficient character-based communication systems. In this paper, we propose a framework to systematically simplify multi-stroke characters by selectively removing strokes while preserving their overall legibility. More specifically, we use a highly accurate character recognition model to assess legibility and remove those strokes that minimally impact it. Experimental results on 1,256 character classes with 5, 10, 15, and 20 strokes reveal several key findings, including the observation that even after removing multiple strokes, many characters remain distinguishable. These findings suggest the potential for more formalized simplification strategies. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_23106 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Computer-Aided Multi-Stroke Character Simplification by Stroke Removal Ishiyama, Ryo Matsuo, Shinnosuke Uchida, Seiichi Computer Vision and Pattern Recognition Multi-stroke characters in scripts such as Chinese and Japanese can be highly complex, posing significant challenges for both native speakers and, especially, non-native learners. If these characters can be simplified without degrading their legibility, it could reduce learning barriers for non-native speakers, facilitate simpler and legible font designs, and contribute to efficient character-based communication systems. In this paper, we propose a framework to systematically simplify multi-stroke characters by selectively removing strokes while preserving their overall legibility. More specifically, we use a highly accurate character recognition model to assess legibility and remove those strokes that minimally impact it. Experimental results on 1,256 character classes with 5, 10, 15, and 20 strokes reveal several key findings, including the observation that even after removing multiple strokes, many characters remain distinguishable. These findings suggest the potential for more formalized simplification strategies. |
| title | Computer-Aided Multi-Stroke Character Simplification by Stroke Removal |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2506.23106 |