# GriTS: Grid table similarity metric for table structure recognition

arXiv: https://arxiv.org/abs/2203.12555 · HTML: https://mlsanity.com/paper/2203.12555/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 23 references that are in the map.
- First posted on arXiv 23 Mar 2022.

## Builds on (most cited first, 3 of 3)

- [Image-based table recognition: data, model, and evaluation](https://mlsanity.com/paper/1911.10683/) (2019)
- [PubTables-1M: Towards comprehensive table extraction from unstructured documents](https://mlsanity.com/paper/2110.00061/) (2021)
- [TableBank: A Benchmark Dataset for Table Detection and Recognition](https://mlsanity.com/paper/1903.01949/) (2019)

## Built on by (most cited first, 28 of 28)

- [PaliGemma 2: A Family of Versatile VLMs for Transfer](https://mlsanity.com/paper/2412.03555/) (2024)
- [TabPedia: Towards Comprehensive Visual Table Understanding with Concept Synergy](https://mlsanity.com/paper/2406.01326/) (2024)
- [Improving Table Structure Recognition with Visual-Alignment Sequential Coordinate Modeling](https://mlsanity.com/paper/2303.06949/) (2023)
- [Deep learning for table detection and structure recognition: A survey](https://mlsanity.com/paper/2211.08469/) (2022)
- [Aligning benchmark datasets for table structure recognition](https://mlsanity.com/paper/2303.00716/) (2023)
- [SEMv2: Table Separation Line Detection Based on Instance Segmentation](https://mlsanity.com/paper/2303.04384/) (2023)
- [UniTabNet: Bridging Vision and Language Models for Enhanced Table Structure Recognition](https://mlsanity.com/paper/2409.13148/) (2024)
- [ParseBench: A Document Parsing Benchmark for AI Agents](https://mlsanity.com/paper/2604.08538/) (2026)
- [SPRINT: Script-agnostic Structure Recognition in Tables](https://mlsanity.com/paper/2503.11932/) (2025)
- [Retrieval Augmented Structured Generation: Business Document Information Extraction As Tool Use](https://mlsanity.com/paper/2405.20245/) (2024)
- [RAPTOR: Refined Approach for Product Table Object Recognition](https://mlsanity.com/paper/2502.14918/) (2025)
- [LLM-as-a-Judge Scores Are Unreliable Optimization Signals in Closed-Loop Table Recognition](https://mlsanity.com/paper/2607.13347/) (2026)
- [QUEST: Quality-aware Semi-supervised Table Extraction for Business Documents](https://mlsanity.com/paper/2506.14568/) (2025)
- [PubTables-v2: A new large-scale dataset for full-page and multi-page table extraction](https://mlsanity.com/paper/2512.10888/) (2025)
- [Beyond String Matching: Semantic Evaluation of PDF Table Extraction](https://mlsanity.com/paper/2603.18652/) (2026)
- [TABVERSE: Benchmarking Cross-Format Table Understanding in LLMs and VLMs](https://mlsanity.com/paper/2606.09578/) (2026)
- [Building Agent Harnesses for Scientific Curation from Multimodal Sources](https://mlsanity.com/paper/2606.21005/) (2026)
- [DenTab: A Dataset for Table Recognition and Visual QA on Real-World Dental Estimates](https://mlsanity.com/paper/2604.16099/) (2026)
- [LIFT: Last-Mile Fine-Tuning for Table Explicitation](https://mlsanity.com/paper/2605.13424/) (2026)
- [PulseBench-Tab: A Multilingual Benchmark for Table Extraction with Graph-Based Evaluation](https://mlsanity.com/paper/2606.07534/) (2026)
- [POTATR: A Lightweight Image-to-Graph Model for Page-Level Table Extraction](https://mlsanity.com/paper/2606.09788/) (2026)
- [Rethinking the Pointer Loss in Table Structure Recognition: Geometry-Aware Pointer Loss for Spatial Locality](https://mlsanity.com/paper/2606.18721/) (2026)
- [ConRTF: Edge-Constrained Boundary Distribution Refinement for Realtime TransFormer Table Structure Recognition](https://mlsanity.com/paper/2607.00734/) (2026)
- [Active Learning for Cascaded Object Detection: Balancing Coverage and Uncertainty in Table Extraction Pipelines](https://mlsanity.com/paper/2607.00747/) (2026)
- [FormStruct-Bench:A Hierarchical and Diagnostic Benchmark for Table-Form Document Structure Recognition](https://mlsanity.com/paper/2608.10396/) (2026)
- [Tables Decoded: DELTA for Structure, TARQA for Understanding](https://mlsanity.com/paper/2609.17458/) (2026)
- [Layout-Guided Masking for GROBID: Lightweight Structural Gains in Large-Scale Scientific PDF Ingestion](https://mlsanity.com/paper/2609.26381/) (2026)
- [TableVision: Recovering Verified Structure from Historical Tables](https://mlsanity.com/paper/2610.17448/) (2026)

## Abstract (arXiv)

In this paper, we propose a new class of metric for table structure recognition (TSR) evaluation, called 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. This algorithm produces both an upper and a lower bound on the true similarity between matrices. We show using evaluation on a large real-world dataset that in practice there is almost no difference between these bounds. We compare GriTS to other metrics and empirically validate that matrix similarity exhibits more desirable behavior than alternatives for TSR performance evaluation. Finally, GriTS 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 TSR approaches. Code will be released at https://github.com/microsoft/table-transformer.