GridFormer: Towards Accurate Table Structure Recognition via Grid Prediction

• URL: http://arxiv.org/abs/2309.14962v1
• Date: Tue, 26 Sep 2023 14:29:45 GMT
• Title: GridFormer: Towards Accurate Table Structure Recognition via Grid Prediction
• Authors: Pengyuan Lyu, Weihong Ma, Hongyi Wang, Yuechen Yu, Chengquan Zhang, Kun Yao, Yang Xue, Jingdong Wang
• Abstract summary: We propose GridFormer, a novel approach for interpreting unconstrained table structures. In this paper, we propose a flexible table representation in the form of an MXN grid. Then, we introduce a DETR-style table structure recognizer to efficiently predict this multi-objective information of the grid in a single shot.
• Score: 35.15882175670814
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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