  Writer identification (writer-id), an important field in biometrics, aims to
identify a writer by their handwriting. Identification in existing writer-id
studies requires a complete document or text, limiting the scalability and
flexibility of writer-id in realistic applications. To make the application of
writer-id more practical (e.g., on mobile devices), we focus on a novel
problem, letter-level online writer-id, which requires only a few trajectories
of written letters as identification cues. Unlike text-\ document-based
writer-id which has rich context for identification, there are much fewer clues
to recognize an author from only a few single letters. A main challenge is that
a person often writes a letter in different styles from time to time. We refer
to this problem as the variance of online writing styles (Var-O-Styles). We
address the Var-O-Styles in a capture-normalize-aggregate fashion: Firstly, we
extract different features of a letter trajectory by a carefully designed
multi-branch encoder, in an attempt to capture different online writing styles.
Then we convert all these style features to a reference style feature domain by
a novel normalization layer. Finally, we aggregate the normalized features by a
hierarchical attention pooling (HAP), which fuses all the input letters with
multiple writing styles into a compact feature vector. In addition, we also
contribute a large-scale LEtter-level online wRiter IDentification dataset
(LERID) for evaluation. Extensive comparative experiments demonstrate the
effectiveness of the proposed framework.
