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-\(\backslash \) 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.

作家识别（writer-id）是生物识别学中的一个重要领域，旨在通过手写识别作者。现有作家ID研究中的识别需要完整的文档或文本，从而限制了作家ID在实际应用中的可伸缩性和灵活性。为了使writer-id的应用更加实用（例如，在移动设备上），我们将重点放在一个新颖的问题上，即字母级的在线writer-id，该问题仅需要几个书写字母的轨迹作为识别线索。与文字不同- \（\反斜杠\）基于文档的writer-id具有丰富的标识上下文，只有很少的单个字母来识别作者的线索很少。一个主要的挑战是一个人经常不时以不同的风格写信。我们将此问题称为在线写作风格（Var-O-Styles）的差异。我们以捕获归一化聚合的方式处理Var-O-Styles：首先，我们通过精心设计的多分支编码器提取字母轨迹的不同特征，以尝试捕获不同的在线书写风格。然后，我们通过新颖的归一化层将所有这些样式特征转换为参考样式特征域。最后，我们通过层次化注意力集中来聚合归一化特征（HAP），它将所有具有多种书写样式的输入字母融合为紧凑的特征向量。另外，我们还为评估提供了大规模的LETER级在线witer IDentification数据集（LERID）。大量的比较实验证明了所提出框架的有效性。