Computer-Aided Multi-Stroke Character Simplification by Stroke Removal
This work addresses the challenge of reducing learning barriers for non-native speakers and improving font design, but it is incremental as it builds on existing recognition models for simplification.
The paper tackles the problem of simplifying complex multi-stroke characters in scripts like Chinese and Japanese by selectively removing strokes while preserving legibility, using a character recognition model to assess impact, and finds that many characters remain distinguishable even after multiple strokes are removed.
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.