Instance GNN: A Learning Framework for Joint Symbol Segmentation and Recognition in Online Handwritten Diagrams
文献类型：期刊论文
| 作者 | Yun, Xiao-Long3,4; Zhang, Yan-Ming3 ; Yin, Fei3; Liu, Cheng-Lin1,2,3
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| 刊名 | IEEE TRANSACTIONS ON MULTIMEDIA
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| 出版日期 | 2022 |
| 卷号 | 24页码:2580-2594 |
| 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 |
| ISSN号 | 1520-9210 |
| DOI | 10.1109/TMM.2021.3087000 |
| 通讯作者 | Liu, Cheng-Lin(liucl@nlpr.ia.ac.cn) |
| 英文摘要 | 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. |
| WOS关键词 | CLASSIFICATION |
| 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] |
| WOS研究方向 | Computer Science ; Telecommunications |
| 语种 | 英语 |
| WOS记录号 | WOS:000793839600026 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 资助机构 | Major Project for New Generation of AI ; National Natural Science Foundation of China (NSFC) |
| 源URL | [http://ir.ia.ac.cn/handle/173211/49452]  |
| 专题 | 自动化研究所_模式识别国家重点实验室_模式分析与学习团队 |
| 通讯作者 | Liu, Cheng-Lin |
| 作者单位 | 1.Chinese Acad Sci, Ctr Excellence Brain Sci & Intelligence Technol, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 3.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China 4.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China |
| 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. |
入库方式： OAI收割
来源：自动化研究所
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