Most existing online writer-identification systems require that the text
content is supplied in advance and rely on separately designed features and
classifiers. The identifications are based on lines of text, entire paragraphs,
or entire documents; however, these materials are not always available. In this
paper, we introduce a path-signature feature to an end-to-end text-independent
writer-identification system with a deep convolutional neural network (DCNN).
Because deep models require a considerable amount of data to achieve good
performance, we propose a data-augmentation method named DropStroke to enrich
personal handwriting. Experiments were conducted on online handwritten Chinese
characters from the CASIA-OLHWDB1.0 dataset, which consists of 3,866 classes
from 420 writers. For each writer, we only used 200 samples for training and
the remaining 3,666. The results reveal that the path-signature feature is
useful for writer identification, and the proposed DropStroke technique
enhances the generalization and significantly improves performance.