Currently, the prevalence of online handwriting has spurred a critical need for effective retrieval systems to accurately search relevant handwriting instances from specific writers, known as online writer retrieval. Despite the growing demand, this field suffers from a scarcity of well-established methodologies and public large-scale datasets. This paper tackles these challenges with a focus on Chinese handwritten phrases. First, we propose DOLPHIN, a novel retrieval model designed to enhance handwriting representations through synergistic temporal-frequency analysis. For frequency feature learning, we propose the HFGA block, which performs gated cross-attention between the vanilla temporal handwriting sequence and its high-frequency sub-bands to amplify salient writing details. For temporal feature learning, we propose the CAIR block, tailored to promote channel interaction and reduce channel redundancy. Second, to address data deficit, we introduce OLIWER, a large-scale online writer retrieval dataset encompassing over 670,000 Chinese handwritten phrases from 1,731 individuals. Through extensive evaluations, we demonstrate the superior performance of DOLPHIN over existing methods. In addition, we explore cross-domain writer retrieval and reveal the pivotal role of increasing feature alignment in bridging the distributional gap between different handwriting data. Our findings emphasize the significance of point sampling frequency and pressure features in improving handwriting representation quality and retrieval performance. Code and dataset are available at https://github.com/SCUT-DLVCLab/DOLPHIN.

翻译：当前，在线手写的普及催生了对高效检索系统的迫切需求，以准确搜索特定书写者的相关手写实例，即在线笔迹检索。尽管需求日益增长，该领域仍面临成熟方法稀缺和公开大规模数据集匮乏的挑战。本文聚焦中文手写短语，旨在应对这些挑战。首先，我们提出DOLPHIN，一种新颖的检索模型，通过协同的时频分析增强手写表示。在频率特征学习方面，我们提出HFGA模块，该模块在原始时域手写序列与其高频子带之间执行门控交叉注意力，以放大显著的书写细节。在时域特征学习方面，我们提出CAIR模块，专门用于促进通道交互并减少通道冗余。其次，为应对数据短缺问题，我们引入OLIWER，一个大规模在线笔迹检索数据集，包含来自1,731位个体的超过670,000个中文手写短语。通过广泛评估，我们证明了DOLPHIN相较于现有方法的优越性能。此外，我们探索了跨域笔迹检索，并揭示了增强特征对齐在弥合不同手写数据分布差异中的关键作用。我们的研究结果强调了点采样频率和压力特征在提升手写表示质量和检索性能方面的重要性。代码和数据集可在https://github.com/SCUT-DLVCLab/DOLPHIN获取。