论文标题
评估表结构识别：一种新的视角
Evaluating Table Structure Recognition: A New Perspective
论文作者
论文摘要
用于评估表结构识别算法的现有指标在捕获文本和空细胞对齐方面存在缺点。在本文中，我们以先前的工作为基础，并提出了一个新的度量标准的IOU相似性（TEDS（iou）），用于表结构识别，该识别使用边界框而不是文本，同时对上述劣势也有强大的强大。我们通过各种示例证明了对以前的度量标准的有效性。
Existing metrics used to evaluate table structure recognition algorithms have shortcomings with regard to capturing text and empty cells alignment. In this paper, we build on prior work and propose a new metric - TEDS based IOU similarity (TEDS (IOU)) for table structure recognition which uses bounding boxes instead of text while simultaneously being robust against the above disadvantages. We demonstrate the effectiveness of our metric against previous metrics through various examples.
