Abstract
This paper investigates the task of writer retrieval, which identifies documents authored by the same individual within a dataset based on handwriting similarities. While existing datasets and methodologies primarily focus on page level retrieval, we explore the impact of text quantity on writer retrieval performance by evaluating line- and word level retrieval. We examine three state-of-the-art writer retrieval systems, including both handcrafted and deep learning-based approaches, and analyze their performance using varying amounts of text. Our experiments on the CVL and IAM dataset demonstrate that while performance decreases by 20-30% when only one line of text is used as query and gallery, retrieval accuracy remains above 90% of full-page performance when at least four lines are included. We further show that text-dependent retrieval can maintain strong performance in low-text scenarios. Our findings also highlight the limitations of handcrafted features in low-text scenarios, with deep learning-based methods like NetVLAD outperforming traditional VLAD encoding.
Abstract (translated)
本文探讨了书写者检索任务，该任务基于手写相似性在数据集中识别由同一人创作的文档。虽然现有的数据集和方法主要集中在页面级别的检索上，我们通过评估行级和词级检索来研究文本量对书写者检索性能的影响。我们分析了三种最先进的书写者检索系统（包括手工设计和深度学习基于的方法），并使用不同数量的文本进行性能测试。我们在CVL和IAM数据集上的实验表明，当仅使用一行文本作为查询和检索库时，性能会下降20-30%，但如果至少包含四行文本，检索准确率仍然可以保持在全页表现的90%以上。此外，我们还展示了基于文本依赖性的检索可以在低文本场景中维持较强的性能。我们的研究结果进一步强调了手工设计特征在低文本环境中的局限性，表明如NetVLAD这样的深度学习方法优于传统的VLAD编码方法。
URL
https://arxiv.org/abs/2506.07566
