We introduce a new table detection and structure recognition approach named RobusTabNet to detect the boundaries of tables and reconstruct the cellular structure of each table from heterogeneous document images. For table detection, we propose to use CornerNet as a new region proposal network to generate higher quality table proposals for Faster R-CNN, which has significantly improved the localization accuracy of Faster R-CNN for table detection. Consequently, our table detection approach achieves state-of-the-art performance on three public table detection benchmarks, namely cTDaR TrackA, PubLayNet and IIIT-AR-13K, by only using a lightweight ResNet-18 backbone network. Furthermore, we propose a new split-and-merge based table structure recognition approach, in which a novel spatial CNN based separation line prediction module is proposed to split each detected table into a grid of cells, and a Grid CNN based cell merging module is applied to recover the spanning cells. As the spatial CNN module can effectively propagate contextual information across the whole table image, our table structure recognizer can robustly recognize tables with large blank spaces and geometrically distorted (even curved) tables. Thanks to these two techniques, our table structure recognition approach achieves state-of-the-art performance on three public benchmarks, including SciTSR, PubTabNet and cTDaR TrackB2-Modern. Moreover, we have further demonstrated the advantages of our approach in recognizing tables with complex structures, large blank spaces, as well as geometrically distorted or even curved shapes on a more challenging in-house dataset.
翻译:我们引入了名为 RobusTabNet 的新的表格检测和结构识别方法, 以探测表格的界限, 并从不同文档图像中重建每张表格的细胞结构。 为了检测表格, 我们提议使用CornerNet作为新的区域建议网络, 为更快的 R- CNN 生成更高质量的表格建议, 这大大提高了快速 R- CNN 的本地化精确度, 用于表格检测。 因此, 我们的表格检测方法在三个公开表格检测基准, 即 CTDaR TrackA、 PubLayNet 和 IIIT- AR- 13K 上取得了最先进的性能, 只需使用一个轻度的 ResNet-18 主干网 。 此外, 我们建议使用一个新的基于分裂和合并的表格结构, 新的基于空间CNN 的分隔线预测模块, 将每个检测到的表格分成一个单元格网格, 而基于网络的细胞组合组合组合组合组合组合, 空间CNN 模块可以有效地在整个表格中传播背景信息, 我们的系统结构可以强有力地辨识辨辨辨地识别大空空空空的表格和几度扭曲的表格( ) 。, 以Pucricruder2- tral- tral- t- t- sal- sal- sal- sal- lab lab lab lab lab lab lab lab lab lab lab lab lab lab lab lab lab lad- lab lab lab lab lad- lab lab lab lab lad- lab lab lab lad lad lab lab