Towards the Influence of Text Quantity on Writer Retrieval

• URL: http://arxiv.org/abs/2506.07566v1
• Date: Mon, 09 Jun 2025 09:05:15 GMT
• Title: Towards the Influence of Text Quantity on Writer Retrieval
• Authors: Marco Peer, Robert Sablatnig, Florian Kleber,
• Abstract summary: Writer retrieval identifies documents authored by the same individual within a dataset based on handwriting similarities.<n>We examine three state-of-the-art writer retrieval systems, including both handcrafted and deep learning-based approaches.
• Score: 1.024113475677323
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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