表格信息抽取介绍

随着大量应用程序、工具和在线平台在当今技术时代的蓬勃发展,收集的数据量正在急剧增加。为了有效地处理和访问这些庞大的数据,有必要开发有价值的信息提取工具。在信息提取领域中,表格数据的提取和访问是一个需要关注的子领域。

目录

  • 表格抽取介绍(Introduction to Table Extraction)
  • 谁将发现表格抽取的有用?(Who will find Table Extraction Useful?)
    • 个人使用案例(Personal use cases)
    • 工业使用案例(Industrial use cases)
    • 商业使用案例(Business use cases)
  • 深度学习实战(Deep Learning in Action)
    • TableNet
    • DeepDeSRT
    • Graph Neural Networks
    • CGANs and Genetic Algorithms
  • 传统方法(Traditional Approaches)
    • 基于OpenCV的表格检测(Table Detection with OpenCV)
    • PDFMiner and Regex parsing
  • 传统方法挑战(Challenges with Traditional Methods)
    • 表格检测(Table Detection)
    • 表格提取(Table Extraction)
    • 表格变换(Table Conversion)
  • 总结(Summary)
  • 基于Nanonets的OCR(OCR with Nanonets)
  • Nanonets and Humans in the Loop
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机器学习的一个分支,它基于试图使用包含复杂结构或由多重非线性变换构成的多个处理层对数据进行高层抽象的一系列算法。

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