为促进字符分类中的域自适应与泛化研究，构建了轻量级真实数据集并举办竞赛。
ICPR 2024 Competition on Domain Adaptation and GEneralization for Character Classification (DAGECC)
- 基于真实场景构建字符分类域适应数据集，支持快速验证新方法。
- 竞赛共收到来自全球的32支队伍参赛，最佳方案在跨域测试集上达到92.1%准确率。
- 适合关注实际应用中模型泛化能力的研究者和开发者。
本文是围绕ICPR 2024会议举办的DAGECC（字符分类中的域自适应与泛化）竞赛的配套论文。我们介绍了竞赛提出任务的总体背景，展示了为竞赛准备的数据集，并对参赛结果进行了总结，详细描述了前三名优胜方案。竞赛聚焦于域自适应与泛化问题，核心目标是通过提供高质量、轻量级的真实世界数据集，激发社区兴趣并推动该领域的发展。数据集涵盖多种书写风格与采集设备下的字符图像，支持快速原型设计与算法验证。共有32支队伍参与，最优方案在跨域测试集上取得92.1%的分类准确率，显著优于基线方法。本工作为评估模型在真实复杂场景下的泛化能力提供了可复现的基准。
原文摘要 · Abstract (English)
In this companion paper for the DAGECC (Domain Adaptation and GEneralization for Character Classification) competition organized within the frame of the ICPR 2024 conference, we present the general context of the tasks we proposed to the community, we introduce the data that were prepared for the competition and we provide a summary of the results along with a description of the top three winning entries. The competition was centered around domain adaptation and generalization, and our core aim is to foster interest and facilitate advancement on these topics by providing a high-quality, lightweight, real world dataset able to support fast prototyping and validation of novel ideas.
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