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Classifying Structural Features of a Digital Document by Feature Type using Machine Learning

机译:使用机器学习按特征类型对数字文档的结构特征进行分类

摘要

Classifying structural features of a digital document by feature type using machine learning is leveraged in a digital medium environment. A document analysis system is leveraged to extract structural features from digital documents, and to classifying the structural features by respective feature types. To do this, the document analysis system employs a character analysis model and a classification model. The character analysis model takes text content from a digital document and generates text vectors that represent the text content. A vector sequence is generated based on the text vectors and position information for structural features of the digital document, and the classification model processes the vector sequence to classify the structural features into different feature types. The document analysis system can generate a modifiable version of the digital document that enables its structural features to be modified based on their respective feature types.
机译:在数字媒体环境中,利用机器学习按特征类型对数字文档的结构特征进行分类。利用文档分析系统从数字文档中提取结构特征,并通过相应的特征类型对结构特征进行分类。为此,文档分析系统采用字符分析模型和分类模型。字符分析模型从数字文档中获取文本内容,并生成代表文本内容的文本向量。基于文本向量和用于数字文档的结构特征的位置信息生成向量序列,分类模型处理该向量序列以将结构特征分类为不同的特征类型。文档分析系统可以生成数字文档的可修改版本,从而使其结构特征可以根据其各自的特征类型进行修改。

著录项

  • 公开/公告号US2020302016A1

    专利类型

  • 公开/公告日2020-09-24

    原文格式PDF

  • 申请/专利权人 ADOBE INC.;

    申请/专利号US201916359402

  • 发明设计人 MILAN AGGARWAL;BALAJI KRISHNAMURTHY;

    申请日2019-03-20

  • 分类号G06F17/27;G06F16/93;G06N3/08;G06N3/04;G10L15/02;

  • 国家 US

  • 入库时间 2022-08-21 11:24:30

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