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引用次数: 11
摘要
逻辑结构的恢复是一个非常重要的任务，但无论是基于图像的文档，还是原生数字文档系统都没有解决这个问题。本文提出了一种基于二维条件随机场的上下文信息建模方法，用于学习数字固定布局文档的页面结构。通过对可移植文档格式(Portable Document Format, PDF)内容和布局的启发式先验知识进行解释，构建邻域图和各种对组合模板，用于多上下文的建模。通过整合从PDF属性中获得的局部和上下文观察，可以更好地解决语义标签的模糊性。实验比较了六种类型的团模板，证明了上下文信息在16个精细定义类别的逻辑标签中的好处。
Logical Labeling of Fixed Layout PDF Documents Using Multiple Contexts
The task of logical structure recovery is known to be of crucial importance, yet remains unsolved not only for image based document but also for born-digital document system. In this work, the modeling of contextual information based on 2D Conditional Random Fields is proposed to learn page structure for born-digital fixed-layout documents. Heuristic prior knowledge of Portable Document Format (PDF) content and layout are interpreted to construct neighborhood graphs and various pair wise clique templates for the modeling of multiple contexts. By integrating local and contextual observations obtained from PDF attributes, the ambiguities of semantic labels are better resolved. Experimental comparisons for six types of clique templates has demonstrated the benefits of contextual information in logical labeling of 16 finely defined categories.
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