On-line handwritten character segmentation is often associated with
handwriting recognition and even though recognition models include mechanisms
to locate relevant positions during the recognition process, it is typically
insufficient to produce a precise segmentation. Decoupling the segmentation
from the recognition unlocks the potential to further utilize the result of the
recognition. We specifically focus on the scenario where the transcription is
known beforehand, in which case the character segmentation becomes an
assignment problem between sampling points of the stylus trajectory and
characters in the text. Inspired by the $k$-means clustering algorithm, we view
it from the perspective of cluster assignment and present a Transformer-based
architecture where each cluster is formed based on a learned character query in
the Transformer decoder block. In order to assess the quality of our approach,
we create character segmentation ground truths for two popular on-line
handwriting datasets, IAM-OnDB and HANDS-VNOnDB, and evaluate multiple methods
on them, demonstrating that our approach achieves the overall best results.

在线手写字符分割与手写识别紧密关联，但仅通过识别模型定位相关位置通常无法产生精确的分割。我们将分割与识别解耦，使得分割结果能进一步被利用。我们专注于已知转录的情况，此时字符分割变成了笔迹轨迹的采样点与文本中字符的匹配问题。受 $k$-means 聚类算法启发，我们从簇分配的角度看待它，并提出了一种基于 Transformer 的架构，在 Transformer 解码器块中使用学得的字符查询形成每个簇。为了评估我们方法的质量，我们为两个流行的在线手写数据集 IAM-OnDB 和 HANDS-VNOnDB 创建了字符分割的真值，并对它们进行了多种方法的评估，证明我们的方法达到了最佳的整体效果。