一种基于视觉变压器的智能文本提取方法,用于金融发票.
1School of Innovation and Entrepreneurship, Zhengzhou University of Science and Technology, Zhengzhou 450064, China.
Mathematical biosciences and engineering : MBE
|December 5, 2023
概括
本研究介绍了一种使用视觉变压器来提取财务发票文本的深度学习模型. 该方法提高了从发票中提取关键信息的准确性和稳定性.
科学领域:
- 计算机视觉 计算机视觉
- 深度学习 (Deep Learning) 是一种深度学习.
- 自然语言处理自然语言处理.
背景情况:
- 传统的金融发票文本提取依赖于手动功能工程,限制了适用性和稳定性.
- 现有的方法难以处理各种发票布局,需要特定场景的开发.
- 大数据时代需要更适应性和通用的解决方案来自动提取数据.
研究的目的:
- 开发一种轻量级和强大的深度学习模型,用于从财务发票中以计算机视觉辅助的文本提取.
- 利用深度学习的适应性特征学习能力,克服传统方法的局限性.
- 引入一种使用视觉变压器进行改进的金融发票信息提取的新方法.
主要方法:
- 使用图像处理技术预处理财务发票图像.
- 采用序列传导模型与视觉变压器架构用于信息提取.
- 使用横垂直投影进行字符分割和模板匹配进行字符规范化.
- 在变压器结构中应用多头注意力机制,以捕获细粒度特征关系.
- 实施文本分类程序以输出检测结果.
主要成果:
- 拟议的基于视觉变压器的模型与传统方法相比,表现出优越的性能.
- 在真实世界数据集上的实验结果证实了在提取财务发票信息时的高准确性.
- 该方法在各种金融发票场景中表现出显著的稳定性.
结论:
- 深度学习,特别是视觉转换器,为增强金融发票文本提取提供了强大的解决方案.
- 开发的模型为传统的图像处理技术提供了更具普遍性和强大的替代方案.
- 这种方法显著提高了自动化金融数据提取的效率和可靠性.
相关概念视频
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01:21Energy Losses in Transformers
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01:27Transformers in Distribution System
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