Abstract
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 this https URL.
Abstract (translated)
当前，在线手写内容的普及引发了对于能够准确搜索特定作者相关手写实例的有效检索系统的关键需求，这被称为在线作家检索。尽管需求不断增长，该领域仍缺乏成熟的方法论和大规模公共数据集。本文针对这些挑战，重点研究中文手写短语的问题。首先，我们提出了DOLPHIN模型，这是一种新型的检索模型，旨在通过协同的时间-频率分析来增强手写表示。为了学习频率特征，我们提出了HFGA模块，该模块在普通时间手写序列与其高频子带之间执行门控交叉注意力机制，以放大显著的书写细节。对于时间特征的学习，我们提出了CAIR模块，专门设计用于促进通道交互并减少通道冗余。其次，为了解决数据不足的问题，我们引入了OLIWER，这是一个大型在线作家检索数据集，包含来自1731个个体的超过670,000个中文手写短语。通过广泛的评估，我们展示了DOLPHIN模型在现有方法上的优越性能。此外，我们也探索了跨域作家检索，并揭示了提高特征对齐在缩小不同手写数据分布差距中的关键作用。我们的发现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意义。代码和数据集可在以下链接获取：[此https URL]。
URL
https://arxiv.org/abs/2412.11668
