2023/09/26 by Pengyuan Lyu, Weihong Ma, Lyu, Pengyuan +13 · 3 citations
Computer Science · Decision Sciences · #Computer Vision and Pattern Recognition (cs.CV) #Data Quality and Management #FOS: Computer and information sciences #Time Series Analysis and Forecasting #Web Data Mining and Analysis
paper · pdf · doi:10.48550/arxiv.2309.14962
openalex publication_date 2023/09/26 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.