Self-supervised learning offers an efficient way of extracting rich
representations from various types of unlabeled data while avoiding the cost of
annotating large-scale datasets. This is achievable by designing a pretext task
to form pseudo labels with respect to the modality and domain of the data.
Given the evolving applications of online handwritten texts, in this study, we
propose the novel Part of Stroke Masking (POSM) as a pretext task for
pretraining models to extract informative representations from the online
handwriting of individuals in English and Chinese languages, along with two
suggested pipelines for fine-tuning the pretrained models. To evaluate the
quality of the extracted representations, we use both intrinsic and extrinsic
evaluation methods. The pretrained models are fine-tuned to achieve
state-of-the-art results in tasks such as writer identification, gender
classification, and handedness classification, also highlighting the
superiority of utilizing the pretrained models over the models trained from
scratch.

通过自我监督学习从未标注的数据中提取丰富的表示，避免了对大规模数据集的标注成本。本研究针对在线手写文本的不断发展应用，提出了一种新颖的基于笔画部分遮罩的预训练任务 (POSM)，用于从英语和汉语两种语言的个人在线手写中提取信息丰富的表示，以及两种建议的微调预训练模型的流程。为了评估提取表示的质量，我们使用内在和外在评估方法。微调预训练模型可实现诸如作者识别、性别分类和利手性分类等任务的最新成果，并凸显了利用预训练模型优于从头开始训练模型的优点。