复杂表格结构识别
Complicated Table Structure Recognition
摘要：
表结构识别的任务是识别表的内部结构，这是机器理解表的关键步骤。目前，针对不同的文件格式，如ASCII文本和HTML，有很多关于此任务的研究。识别PDF文件中的表格结构也是一个非常重要的问题。然而，现有的方法很难准确识别PDF文件中复杂表格的结构。复杂的表格包含至少占用两列或两行的跨单元格。为了解决这个问题，我们提出了一种新的图神经网络识别PDF文件中的表格结构，命名为GraphTSR。具体地说，它以表格单元格为输入，然后通过预测单元格之间的关系来识别表格结构。此外，为了更好地评估任务，我们从科学论文中构建了一个大规模的表格结构识别数据集，名为SciTSR，其中包含来自PDF文件的15，000个表格及其相应的结构标签。大量的实验表明，我们提出的模型是非常有效的复杂的表，并优于最先进的基准数据集和我们的新构造的数据集的基线。
英文摘要：
The task of table structure recognition aims to recognize the internal structure of a table, which is a key step to make machines understand tables. Currently, there are lots of studies on this task for different file formats such as ASCII text and HTML. It also attracts lots of attention to recognize the table structures in PDF files. However, it is hard for the existing methods to accurately recognize the structure of complicated tables in PDF files. The complicated tables contain spanning cells which occupy at least two columns or rows. To address the issue, we propose a novel graph neural network for recognizing the table structure in PDF files, named GraphTSR. Specifically, it takes table cells as input, and then recognizes the table structures by predicting relations among cells. Moreover, to evaluate the task better, we construct a large-scale table structure recognition dataset from scientific papers, named SciTSR, which contains 15,000 tables from PDF files and their corresponding structure labels. Extensive experiments demonstrate that our proposed model is highly effective for complicated tables and outperforms state-of-the-art baselines over a benchmark dataset and our new constructed dataset.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
