Table detection and cell segmentation in online handwritten documents with graph attention networks

Ying-Jian Liu, Heng Zhang, Xiao-Long Yun, Jun-Yu Ye, Cheng-Lin Liu
{"title":"Table detection and cell segmentation in online handwritten documents with graph attention networks","authors":"Ying-Jian Liu, Heng Zhang, Xiao-Long Yun, Jun-Yu Ye, Cheng-Lin Liu","doi":"10.1145/3444685.3446295","DOIUrl":null,"url":null,"abstract":"In this paper, we propose a multi-task learning approach for table detection and cell segmentation with densely connected graph attention networks in free form online documents. Each online document is regarded as a graph, where nodes represent strokes and edges represent the relationships between strokes. Then we propose a graph attention network model to classify nodes and edges simultaneously. According to node classification results, tables can be detected in each document. By combining node and edge classification resutls, cells in each table can be segmented. To improve information flow in the network and enable efficient reuse of features among layers, dense connectivity among layers is used. Our proposed model has been experimentally validated on an online handwritten document dataset IAMOnDo and achieved encouraging results.","PeriodicalId":119278,"journal":{"name":"Proceedings of the 2nd ACM International Conference on Multimedia in Asia","volume":"49 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2nd ACM International Conference on Multimedia in Asia","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3444685.3446295","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

In this paper, we propose a multi-task learning approach for table detection and cell segmentation with densely connected graph attention networks in free form online documents. Each online document is regarded as a graph, where nodes represent strokes and edges represent the relationships between strokes. Then we propose a graph attention network model to classify nodes and edges simultaneously. According to node classification results, tables can be detected in each document. By combining node and edge classification resutls, cells in each table can be segmented. To improve information flow in the network and enable efficient reuse of features among layers, dense connectivity among layers is used. Our proposed model has been experimentally validated on an online handwritten document dataset IAMOnDo and achieved encouraging results.
基于图关注网络的在线手写文档表检测与单元分割
在本文中,我们提出了一种多任务学习方法,用于自由形式在线文档中密集连接的图关注网络的表检测和单元分割。每个在线文档被视为一个图,其中节点表示笔画,边表示笔画之间的关系。在此基础上,提出了一种同时对节点和边进行分类的图关注网络模型。根据节点分类结果,可以在每个文档中检测到表。通过结合节点和边缘的分类结果,可以对每个表中的单元格进行分割。为了改善网络中的信息流,实现层与层之间特征的高效重用,采用了层与层之间的密集连接。我们提出的模型已经在一个在线手写文档数据集IAMOnDo上进行了实验验证,取得了令人鼓舞的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信