DKDS: 一种带有印鉴的退化古文文档基准数据集用于检测和二值化
DKDS: A Benchmark Dataset of Degraded Kuzushiji Documents with Seals for Detection and Binarization
AI总结：
本文提出DKDS数据集，用于解决退化古文文档中印鉴的检测与二值化问题，提供YOLO和GAN等方法的基线结果。
摘要：
Kuzushiji是一种古代日本草书，目前只有少数受过训练的专家能阅读和理解。随着深度学习的发展，研究者开始应用光学字符识别（OCR）技术将Kuzushiji转录为现代日语。尽管现有OCR方法在清洁的古代日本文档上表现良好，但往往忽视各种噪声类型，如文档退化和印鉴，这显著影响识别准确性。本文介绍了一种带有印鉴的退化Kuzushiji文档（DKDS）数据集，作为相关任务的新基准。我们描述了数据集的构建过程，包括受过训练的Kuzushiji专家的帮助，并定义了两个基准任务：（1）Kuzushiji字符和印鉴检测；（2）文档二值化。对于Kuzushiji字符和印鉴检测任务，我们提供了使用几种最新版本的YOLO进行检测的基线结果。对于文档二值化任务，我们展示了传统二值化算法、传统算法结合K-means聚类、两种最先进的（SOTA）生成对抗网络（GAN）方法以及我们改进的基于条件GAN（cGAN）的方法的基线结果。DKDS数据集和基线方法的实现代码可在https://ruiyangju.github.io/DKDS上获得。
英文摘要：
Kuzushiji, a pre-modern Japanese cursive script, can currently be read and understood by only a few thousand trained experts in Japan. With the rapid development of deep learning, researchers have begun applying Optical Character Recognition (OCR) techniques to transcribe Kuzushiji into modern Japanese. Although existing OCR methods perform well on clean pre-modern Japanese documents written in Kuzushiji, they often fail to consider various types of noise, such as document degradation and seals, which significantly affect recognition accuracy. To the best of our knowledge, no existing dataset specifically addresses these challenges. To address this gap, we introduce the Degraded Kuzushiji Documents with Seals (DKDS) dataset as a new benchmark for related tasks. We describe the dataset construction process, which involves the assistance of a trained Kuzushiji expert, and define two benchmark tracks: (1) Kuzushiji character and seal detection and (2) document binarization. For the Kuzushiji character and seal detection track, we provide baseline results using several recent versions of YOLO to detect Kuzushiji characters and seals. For the document binarization track, we present baseline results from traditional binarization algorithms, traditional algorithms combined with K-means clustering, two state-of-the-art (SOTA) generative adversarial network (GAN) methods, and our improved conditional GAN (cGAN)-based method. The DKDS dataset and the implementation code for baseline methods are available at https://ruiyangju.github.io/DKDS.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
