在文档分析Tasks_Application to Table案例中评估性能的新指标

A. C. E. Silva
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引用次数: 12

摘要

在将行分类为表的一部分时具有高精度和召回率的算法是否真的擅长定位表?一些文档分析任务需要粘合或剪切某些文档元素以形成其他文档元素。对于这种分割/聚合任务,通常使用的精度和召回率的适用性是有争议的,因为它们的基本假设是输入项的粒度与输出项的粒度相同。我们提出了特别适合这类任务的新的评估指标,并在几个表任务中展示了它们的应用。在此过程中,我们提出了鲁棒的表定位和单元分割算法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
New Metrics for Evaluating Performance in Document Analysis Tasks_Application to the Table Case
Is an algorithm capable of high precision and recall at classifying lines as part of table really good at locating tables? Several document analysis tasks require gluing or cutting certain document elements to form others. The suitability of the commonly used precision and recall for such division/aggregation tasks is arguable, since their underlying assumption is that the granularity of the items at input is the same as at output. We propose new evaluation metrics especially suited for this type of tasks, and show their application in several table tasks. In the process we present robust table location and cell segmentation algorithms.
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