Knowledge Commons of Institute of Automation,CAS
CASIA-AHCDB: A Large-scale Chinese Ancient Handwritten Characters Database | |
Yue Xu1,2; Fei Yin1,2; Da-Han Wang4; Xu-Yao Zhang1,2; Zhaoxiang Zhang1,2,3; Cheng-Lin Liu1,2,3 | |
2019-09 | |
会议名称 | 2019 15th International Conference on Document Analysis and Recognition |
页码 | 793-798 |
会议日期 | 2019.09.20-2019.09.25 |
会议地点 | Sydney, Australia |
摘要 | This paper introduces a Chinese Ancient Handwritten Characters Database (CASIA-AHCDB) for character recognition research. The database was built by annotating 11,937 pages of Chinese ancient handwritten documents. It consists of more than 2.2 million annotated handwritten character samples of 10,350 categories. According to the source of these documents, the database is divided into two datasets of different styles: Complete Library in Four Sections (AHCDB-style1) and Ancient Buddhist Scriptures (AHCDB-style2). Each dataset can be divided into three parts based on its applications. The first part, called basic category set, contains samples of common categories in two datasets, and is suitable for basic character recognition task. The second part, called enhanced category set, is mainly used for open-set character recognition task based on the basic character recognition. The third part, called the reserved category set, can be used in many pattern recognition tasks in the future. Based on the large category set, the various writing styles and the imbalanced sample number per category, CASIA-AHCDB can also be used for various classification and learning tasks such as transfer learning, few-shot learning. We performed experiments of basic character recognition on the basic category set, and report the results for benchmark. More techniques can be evaluated on this challenging database in the future. |
关键词 | Ancient Documents Handwritten Chinese Characters Character Recognition Transfer Learning |
语种 | 英语 |
七大方向——子方向分类 | 文字识别与文档分析 |
国重实验室规划方向分类 | 视觉信息处理 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/49904 |
专题 | 多模态人工智能系统全国重点实验室_模式分析与学习 |
通讯作者 | Cheng-Lin Liu |
作者单位 | 1.中国科学院自动化研究所, 模式识别国家重点实验室 2.中国科学院大学 3.中国科学院脑科学与智能技术卓越创新中心 4.厦门大学计算机与信息工程学院 |
第一作者单位 | 模式识别国家重点实验室 |
通讯作者单位 | 模式识别国家重点实验室 |
推荐引用方式 GB/T 7714 | Yue Xu,Fei Yin,Da-Han Wang,et al. CASIA-AHCDB: A Large-scale Chinese Ancient Handwritten Characters Database[C],2019:793-798. |
条目包含的文件 | 下载所有文件 | |||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
icdar2019.pdf(493KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。
修改评论