Self-Supervised Representation Learning for Online Handwriting Text
  Classification
Pouya Mehralian Bagher BabaAli Ashena Gorgan Mohammadi
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.