This does not mean confusables.txt is wrong. It means confusables.txt is a visual-similarity claim that has never been empirically validated at scale. Many entries map characters to the same abstract target under NFKC decomposition (mathematical bold A to A, for instance), and the mapping is semantically correct even if the glyphs look nothing alike. But if you treat every confusables.txt entry as equally dangerous for UI security, you are generating massive false positive rates for 96.5% of the dataset.
适用：数据均匀分布，如[0,1)之间的浮点数
。谷歌浏览器【最新下载地址】对此有专业解读
The 'magical' blue flower changing farmers' fortunes in India
Москвичи пожаловались на зловонную квартиру-свалку с телами животных и тараканами18:04