All tables can be represented as grids. Based on this observation, we propose
GridFormer, a novel approach for interpreting unconstrained table structures by
predicting the vertex and edge of a grid. First, we propose a flexible table
representation in the form of an MXN grid. In this representation, the vertexes
and edges of the grid store the localization and adjacency information of the
table. Then, we introduce a DETR-style table structure recognizer to
efficiently predict this multi-objective information of the grid in a single
shot. Specifically, given a set of learned row and column queries, the
recognizer directly outputs the vertexes and edges information of the
corresponding rows and columns. Extensive experiments on five challenging
benchmarks which include wired, wireless, multi-merge-cell, oriented, and
distorted tables demonstrate the competitive performance of our model over
other methods.

基于表格可以表示为网格的观察，我们提出了 GridFormer，一种通过预测网格的顶点和边来解释不受约束的表格结构的新方法。我们引入了一种 DETR 风格的表格结构识别器，通过一次预测获得网格的多目标信息。对包括有线、无线、多合并单元、定向和扭曲表格在内的五个具有挑战性的基准进行的大量实验证明了我们模型相对于其他方法的竞争性能。