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.

RobusTabNet 是一种新的具有表检测和结构识别功能的方法，可检测表格的边界，并从异构文档图像中重建每个表格的细胞结构。我们提出了使用 CornerNet 作为新的区域提议网络进行表检测，并提出了基于分割和合并的表格结构识别方法。

异构文档图像中的鲁棒表格检测和结构识别

Robust Table Detection and Structure Recognition from Heterogeneous  Document Images

Convolutional neural networks (CNNs) are usually built by stacking
convolutional operations layer-by-layer. Although CNN has shown strong
capability to extract semantics from raw pixels, its capacity to capture
spatial relationships of pixels across rows and columns of an image is not
fully explored. These relationships are important to learn semantic objects
with strong shape priors but weak appearance coherences, such as traffic lanes,
which are often occluded or not even painted on the road surface as shown in
Fig. 1 (a). In this paper, we propose Spatial CNN (SCNN), which generalizes
traditional deep layer-by-layer convolutions to slice-byslice convolutions
within feature maps, thus enabling message passings between pixels across rows
and columns in a layer. Such SCNN is particular suitable for long continuous
shape structure or large objects, with strong spatial relationship but less
appearance clues, such as traffic lanes, poles, and wall. We apply SCNN on a
newly released very challenging traffic lane detection dataset and Cityscapse
dataset. The results show that SCNN could learn the spatial relationship for
structure output and significantly improves the performance. We show that SCNN
outperforms the recurrent neural network (RNN) based ReNet and MRF+CNN (MRFNet)
in the lane detection dataset by 8.7% and 4.6% respectively. Moreover, our SCNN
won the 1st place on the TuSimple Benchmark Lane Detection Challenge, with an
accuracy of 96.53%.

本文介绍了一种名为 Spatial CNN (SCNN) 的神经网络，该网络利用了基于特征图的层间切片卷积来实现像素之间的跨行列信息传递，从而加强了卷积神经网络 (CNN) 学习结构输出的能力，特别适用于具有强空间关系但较少外表线索的目标识别任务，例如交通车道检测。实验结果表明，与传统的 RNN 和 MRF+CNN（MRFNet）相比，SCNN 可显著提高车道检测数据集的准确度。