基于分层最优传输的日文汉字字符间距离地图
Distance maps between Japanese kanji characters based on hierarchical optimal transport
AI总结：
提出基于分层最优传输的汉字距离通用框架，通过匹配组件嵌套结构计算相异性，并生成2D汉字地图以辅助日语学习、词典编纂及文本识别。
摘要：
我们引入了一个基于汉字间相异性来分配距离的通用框架。该术语的具体含义可能取决于实际应用。我们唯一的假设是，两个汉字之间的相异性可以充分表示为以最优方式匹配组件嵌套结构所得惩罚的加权平均值。对于匹配成本，我们建议了多个可自由组合或替换为其他模块的模块，包括已配准组件间的相对不平衡墨迹传输、配准所需变换之间的距离，以及预设标签的差异。作为概念验证，我们给出了以此方式获得的汉字距离函数的具体示例。基于此函数，我们通过多维缩放生成了2D汉字地图，并制作了包含100个随机选择的常用汉字及其16个最近邻的表格。我们的汉字距离函数可用于帮助非中日韩背景的日语学习者掌握汉字读写能力。此外，它们可以协助汉字词典编纂者展示其材料，并可用于文本处理和光学字符识别系统中以评估错误发生的可能性。
英文摘要：
We introduce a general framework for assigning distances between kanji based on their dissimilarity. What we mean by this term may depend on the concrete application. The only assumption we make is that the dissimilarity between two kanji is adequately expressed as a weighted mean of penalties obtained from matching nested structures of components in an optimal way. For the cost of matching, we suggest a number of modules that can be freely combined or replaced with other modules, including the relative unbalanced ink transport between registered components, the distance between the transformations required for registration, and the difference in prespecified labels. We give a concrete example of a kanji distance function obtained in this way as a proof of concept. Based on this function, we produce 2D kanji maps by multidimensional scaling and a table of 100 randomly selected Jōjō kanji with their 16 nearest neighbors. Our kanji distance functions can be used to help Japanese learners from non-CJK backgrounds acquire kanji literacy. In addition, they may assist editors of kanji dictionaries in presenting their materials and may serve in text processing and optical character recognition systems for assessing the likelihood of errors.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
