Pre-training transformer with dual-branch context content module for table detection in document images

Q1 Computer Science Virtual Reality Intelligent Hardware Pub Date : 2024-10-01 DOI:10.1016/j.vrih.2024.06.003
{"title":"Pre-training transformer with dual-branch context content module for table detection in document images","authors":"","doi":"10.1016/j.vrih.2024.06.003","DOIUrl":null,"url":null,"abstract":"<div><h3>Background</h3><div>Document images such as statistical reports and scientific journals are widely used in information technology. Accurate detection of table areas in document images is an essential prerequisite for tasks such as information extraction. However, because of the diversity in the shapes and sizes of tables, existing table detection methods adapted from general object detection algorithms, have not yet achieved satisfactory results. Incorrect detection results might lead to the loss of critical information.</div></div><div><h3>Methods</h3><div>Therefore, we propose a novel end-to-end trainable deep network combined with a self-supervised pretraining transformer for feature extraction to minimize incorrect detections. To better deal with table areas of different shapes and sizes, we added a dual-branch context content attention module (DCCAM) to high-dimensional features to extract context content information, thereby enhancing the network's ability to learn shape features. For feature fusion at different scales, we replaced the original 3×3 convolution with a multilayer residual module, which contains enhanced gradient flow information to improve the feature representation and extraction capability.</div></div><div><h3>Results</h3><div>We evaluated our method on public document datasets and compared it with previous methods, which achieved state-of-the-art results in terms of evaluation metrics such as recall and F1-score. <span><span>https://github.com/YongZ-Lee/TD-DCCAM</span><svg><path></path></svg></span></div></div>","PeriodicalId":33538,"journal":{"name":"Virtual Reality Intelligent Hardware","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Virtual Reality Intelligent Hardware","FirstCategoryId":"1093","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2096579624000330","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Computer Science","Score":null,"Total":0}
引用次数: 0

Abstract

Background

Document images such as statistical reports and scientific journals are widely used in information technology. Accurate detection of table areas in document images is an essential prerequisite for tasks such as information extraction. However, because of the diversity in the shapes and sizes of tables, existing table detection methods adapted from general object detection algorithms, have not yet achieved satisfactory results. Incorrect detection results might lead to the loss of critical information.

Methods

Therefore, we propose a novel end-to-end trainable deep network combined with a self-supervised pretraining transformer for feature extraction to minimize incorrect detections. To better deal with table areas of different shapes and sizes, we added a dual-branch context content attention module (DCCAM) to high-dimensional features to extract context content information, thereby enhancing the network's ability to learn shape features. For feature fusion at different scales, we replaced the original 3×3 convolution with a multilayer residual module, which contains enhanced gradient flow information to improve the feature representation and extraction capability.

Results

We evaluated our method on public document datasets and compared it with previous methods, which achieved state-of-the-art results in terms of evaluation metrics such as recall and F1-score. https://github.com/YongZ-Lee/TD-DCCAM
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
采用双分支上下文内容模块的预训练变换器,用于文档图像中的表格检测
背景统计报告和科学期刊等文档图像被广泛应用于信息技术领域。准确检测文档图像中的表格区域是完成信息提取等任务的必要前提。然而,由于表格的形状和大小多种多样,从一般对象检测算法中改编而来的现有表格检测方法尚未取得令人满意的结果。因此,我们提出了一种新颖的端到端可训练深度网络,并结合自监督预训练转换器进行特征提取,以尽量减少错误检测。为了更好地处理不同形状和大小的桌面区域,我们在高维特征中添加了双分支上下文内容关注模块(DCCAM),以提取上下文内容信息,从而增强网络学习形状特征的能力。对于不同尺度的特征融合,我们用多层残差模块取代了原来的 3×3 卷积,该模块包含增强的梯度流信息,从而提高了特征表示和提取能力。结果我们在公共文档数据集上对我们的方法进行了评估,并将其与之前的方法进行了比较,后者在召回率和 F1 分数等评估指标方面取得了最先进的结果。https://github.com/YongZ-Lee/TD-DCCAM。
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 去求助
来源期刊
Virtual Reality  Intelligent Hardware
Virtual Reality Intelligent Hardware Computer Science-Computer Graphics and Computer-Aided Design
CiteScore
6.40
自引率
0.00%
发文量
35
审稿时长
12 weeks
期刊最新文献
Co-salient object detection with iterative purification and predictive optimization CURDIS: A template for incremental curve discretization algorithms and its application to conics Mesh representation matters: investigating the influence of different mesh features on perceptual and spatial fidelity of deep 3D morphable models Music-stylized hierarchical dance synthesis with user control Pre-training transformer with dual-branch context content module for table detection in document images
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1