Knowledge Commons of Institute of Automation,CAS
Instance GNN: A Learning Framework for Joint Symbol Segmentation and Recognition in Online Handwritten Diagrams | |
Yun, Xiao-Long1,2; Zhang, Yan-Ming2![]() ![]() ![]() | |
发表期刊 | IEEE TRANSACTIONS ON MULTIMEDIA
![]() |
ISSN | 1520-9210 |
2022 | |
卷号 | 24页码:2580-2594 |
摘要 | Online handwritten diagram recognition (OHDR) has attracted considerable attention for its potential applications in many areas, but it is a challenging task due to the complex 2D structure, writing style variation, and lack of annotated data. Existing OHDR methods often have limitations in modeling and learning complex contextual relationships. To overcome these challenges, we propose an OHDR method based on graph neural networks (GNNs) in this paper. In particular, we formulate symbol segmentation and symbol recognition as node clustering and node classification problems on stroke graphs and solve the problems jointly under a unified learning framework with a GNN model. This GNN model is denoted as Instance GNN since it gives the symbol instance label as well as the semantic label. Extensive experiments on two flowchart datasets and a finite automata dataset show that our method consistently outperforms previous methods with large margins and achieves state-of-the-art performance. In addition, we release a large-scale annotated online handwritten flowchart dataset, CASIA-OHFC, and provide initial experimental results as a baseline. |
关键词 | Handwriting recognition Task analysis Grammar Semantics Image segmentation Trajectory Text recognition Online handwritten diagram recognition symbol segmentation symbol recognition freehand sketch analysis graph neural networks |
DOI | 10.1109/TMM.2021.3087000 |
关键词[WOS] | CLASSIFICATION |
收录类别 | SCI |
语种 | 英语 |
资助项目 | Major Project for New Generation of AI[2018AAA0100400] ; National Natural Science Foundation of China (NSFC)[61773376] ; National Natural Science Foundation of China (NSFC)[61721004] |
项目资助者 | Major Project for New Generation of AI ; National Natural Science Foundation of China (NSFC) |
WOS研究方向 | Computer Science ; Telecommunications |
WOS类目 | Computer Science, Information Systems ; Computer Science, Software Engineering ; Telecommunications |
WOS记录号 | WOS:000793839600026 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
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/173211/49453 |
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 | Liu, Cheng-Lin |
作者单位 | 1.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 2.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China 3.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 4.Chinese Acad Sci, Ctr Excellence Brain Sci & Intelligence Technol, 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 | Yun, Xiao-Long,Zhang, Yan-Ming,Yin, Fei,et al. Instance GNN: A Learning Framework for Joint Symbol Segmentation and Recognition in Online Handwritten Diagrams[J]. IEEE TRANSACTIONS ON MULTIMEDIA,2022,24:2580-2594. |
APA | Yun, Xiao-Long,Zhang, Yan-Ming,Yin, Fei,&Liu, Cheng-Lin.(2022).Instance GNN: A Learning Framework for Joint Symbol Segmentation and Recognition in Online Handwritten Diagrams.IEEE TRANSACTIONS ON MULTIMEDIA,24,2580-2594. |
MLA | Yun, Xiao-Long,et al."Instance GNN: A Learning Framework for Joint Symbol Segmentation and Recognition in Online Handwritten Diagrams".IEEE TRANSACTIONS ON MULTIMEDIA 24(2022):2580-2594. |
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 | ||
yun2021TMM.pdf(3236KB) | 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