GriTS: Grid table similarity metric for table structure recognition
In this paper, we propose a new class of evaluation metric for table structure recognition, grid table similarity (GriTS). Unlike prior metrics, GriTS evaluates the correctness of a predicted table directly in its natural form as a matrix. To create a similarity measure between matrices, we generalize the two-dimensional largest common substructure (2D-LCS) problem, which is NP-hard, to the 2D most similar substructures (2D-MSS) problem and propose a polynomial-time heuristic for solving it. We validate empirically using the PubTables-1M dataset that comparison between matrices exhibits more desirable behavior than alternatives for table structure recognition evaluation. GriTS also unifies all three subtasks of cell topology recognition, cell location recognition, and cell content recognition within the same framework, which simplifies the evaluation and enables more meaningful comparisons across different types of structure recognition approaches.
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PubTables
June 4, 2021
This project is a large dataset, along with baseline trained machine learning models, for the tasks of table detection and table structure recognition in scientific PDF documents. Current datasets for table structure recognition are small and pre-processed in ways that make them applicable only to a specific model architecture, which has limited progress in data-driven methods for this task. The goal of releasing this dataset is to provide a new large standard benchmark for evaluation and a dataset for training that is large enough for deep models to learn effectively. Doing so would enable significant progress to be made toward machine learning methods for these tasks. The dataset for release is derived entirely from a public dataset, the PubMed Open Access dataset of over one million scientific articles, and specifically from the Commercial-Use Collection subset.