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All tables can be represented as grids. Based on this observation, we proposeGridFormer, a novel approach for interpreting unconstrained table structures bypredicting the vertex and edge of a grid. First, we propose a flexible tablerepresentation in the form of an MXN grid. In this representation, the vertexesand edges of the grid store the localization and adjacency information of thetable. Then, we introduce a DETR-style table structure recognizer toefficiently predict this multi-objective information of the grid in a singleshot. Specifically, given a set of learned row and column queries, therecognizer directly outputs the vertexes and edges information of thecorresponding rows and columns. Extensive experiments on five challengingbenchmarks which include wired, wireless, multi-merge-cell, oriented, anddistorted tables demonstrate the competitive performance of our model overother methods.

GridFormer: Towards Accurate Table Structure Recognition via Grid Prediction