DyGAT: Dynamic stroke classification of online handwritten documents and sketches
文献类型：期刊论文
| 作者 | Yang, Yu-Ting1,2 ; Zhang, Yan-Ming2; Yun, Xiao-Long2; Yin, Fei2; Liu, Cheng-Lin2
|
| 刊名 | PATTERN RECOGNITION
 |
| 出版日期 | 2023-09-01 |
| 卷号 | 141页码:12 |
| ISSN号 | 0031-3203 |
| 关键词 | Stroke classification Sketch semantic segmentation Document layout analysis Diagram recognition Streaming recognition |
| DOI | 10.1016/j.patcog.2023.109564 |
| 通讯作者 | Zhang, Yan-Ming(ymzhang@nlpr.ia.ac.cn) |
| 英文摘要 | 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. |
| WOS关键词 | MODE DETECTION |
| 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] |
| WOS研究方向 | Computer Science ; Engineering |
| 语种 | 英语 |
| 出版者 | ELSEVIER SCI LTD |
| WOS记录号 | WOS:000987045600001 |
| 资助机构 | Major Project for New Genera- tion of AI ; National Natural Science Foundation of China (NSFC) |
| 源URL | [http://ir.ia.ac.cn/handle/173211/53254]  |
| 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.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100190, Peoples R China 2.Chinese Acad Sci, Inst Automation, NLPR, Beijing 100190, Peoples R China |
| 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. |
入库方式： OAI收割
来源：自动化研究所
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