Knowledge Commons of Institute of Automation,CAS
DyGAT: Dynamic stroke classification of online handwritten documents and sketches | |
Yang, Yu-Ting1,2; Zhang, Yan-Ming1; Yun, Xiao-Long1; Yin, Fei1; Liu, Cheng-Lin1 | |
发表期刊 | PATTERN RECOGNITION |
ISSN | 0031-3203 |
2023-09-01 | |
卷号 | 141页码:12 |
摘要 | Online handwriting is widely used in human-machine interface, education, office automation, and so on. Stroke classification for online handwritten documents and sketches aims to divide strokes into several semantic categories and is a necessary step for document recognition and understanding. Previous methods are essentially static in that they have to wait for the user to finish the whole sketch before making prediction. However, in practice, the more user-friendly way is to make real-time prediction as the user is writing. In this paper, we introduce Dynamic Graph ATtention network (DyGAT) to solve the dynamic stroke classification problem. The core of our method is to formalize a document/sketch into a multifeature graph, in which nodes represent strokes, edges represent the relationships between strokes, and multiple nodes are applied to one stroke to control the information flow. The proposed method is general and is applicable to online handwritten data of many types. We conduct experiments on popular public datasets to perform sketch semantic segmentation, document layout analysis and diagram recognition, and experimental results show competitive performance. Particularly, the proposed method achieves stroke classification accuracies which are only slightly lower than those of static classification.(c) 2023 Elsevier Ltd. All rights reserved. |
关键词 | Stroke classification Sketch semantic segmentation Document layout analysis Diagram recognition Streaming recognition |
DOI | 10.1016/j.patcog.2023.109564 |
关键词[WOS] | MODE DETECTION |
收录类别 | SCI |
语种 | 英语 |
资助项目 | Major Project for New Genera- tion of AI[2018AAA010 040 0] ; National Natural Science Foundation of China (NSFC)[61773376] ; National Natural Science Foundation of China (NSFC)[62276258] |
项目资助者 | Major Project for New Genera- tion of AI ; National Natural Science Foundation of China (NSFC) |
WOS研究方向 | Computer Science ; Engineering |
WOS类目 | Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic |
WOS记录号 | WOS:000987045600001 |
出版者 | ELSEVIER SCI LTD |
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 |
是否有论文关联数据集需要存交 | 否 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/53255 |
专题 | 多模态人工智能系统全国重点实验室 |
通讯作者 | Zhang, Yan-Ming |
作者单位 | 1.Chinese Acad Sci, Inst Automation, NLPR, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100190, Peoples R China |
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 | Yang, Yu-Ting,Zhang, Yan-Ming,Yun, Xiao-Long,et al. DyGAT: Dynamic stroke classification of online handwritten documents and sketches[J]. PATTERN RECOGNITION,2023,141:12. |
APA | Yang, Yu-Ting,Zhang, Yan-Ming,Yun, Xiao-Long,Yin, Fei,&Liu, Cheng-Lin.(2023).DyGAT: Dynamic stroke classification of online handwritten documents and sketches.PATTERN RECOGNITION,141,12. |
MLA | Yang, Yu-Ting,et al."DyGAT: Dynamic stroke classification of online handwritten documents and sketches".PATTERN RECOGNITION 141(2023):12. |
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 | ||
yang2022DyGAT.pdf(3180KB) | 期刊论文 | 作者接受稿 | 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