使用VGG-19深度学习模型提取表格数据的表格提取
Muhammad Zahid Iqbal1, Nitish Garg1, Saad Bin Ahmed1
1Faculty of Science and Environmental Studies, Department of Computer Science, Lakehead University, Thunder Bay, ON P7B 5E1, Canada.
Sensors (Basel, Switzerland)
|January 11, 2025
概括
本研究引入了一种深度学习方法,用于从文档图像中提取表列和列,在Marmot数据集上获得最先进的结果. 该方法增强了表格结构识别和数据集注释.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 目前用于表格式数据处理的现有方法与不同的表格布局,风格和噪音作斗争.
- 特定任务的特性和模型架构限制了准确的表结构提取.
研究的目的:
- 开发一个全面的深度学习方法,从包含表的文档图像中精确地提取行和列.
- 改进表格结构识别和解决现有数据集中的局限性.
主要方法:
- 一个结合表检测,结构识别和基于语义规则的行提取的深度学习模型.
- 使用VGG-19的转移学习进行模型微调.
- 通过额外的表格结构注释,包括列检测,增强了Marmot数据集.
主要成果:
- 在Marmot数据集上实现了最先进的表格结构提取性能.
- 证明了拟议的深度学习方法的有效性.
- 成功扩展了"松鼠"数据集的注释范围.
结论:
- 拟议的深度学习方法提供了一个强大的解决方案,用于从文档图像中准确地提取表列和列.
- 增强的Marmot数据集为未来的表理解研究提供了宝贵的资源.
- 转移学习进一步提高了模型在表结构识别方面的表现.
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