GridFormer: Towards Accurate Table Structure Recognition via Grid
  Prediction
Pengyuan Lyu Weihong Ma Hongyi Wang Yuechen Yu Chengquan Zhang Kun Yao Yang Xue Jingdong Wang
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.