# TRUST: An Accurate and End-to-End Table structure Recognizer Using Splitting-based Transformers arXiv: https://arxiv.org/abs/2208.14687 ยท HTML: https://mlsanity.com/paper/2208.14687/ ## Key facts - Cited by 19 papers in the map (data through 14 Oct 2026). - Builds on 21 of its 32 references that are in the map. - First posted on arXiv 31 Aug 2022. ## Builds on (most cited first, 21 of 21) - [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017) - [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015) - [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018) - [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014) - [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015) - [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020) - [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016) - [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015) - [Training Region-based Object Detectors with Online Hard Example Mining](https://mlsanity.com/paper/1604.03540/) (2016) - [Image-based table recognition: data, model, and evaluation](https://mlsanity.com/paper/1911.10683/) (2019) - [Shape Robust Text Detection with Progressive Scale Expansion Network](https://mlsanity.com/paper/1903.12473/) (2019) - [Global Table Extractor (GTE): A Framework for Joint Table Identification and Cell Structure Recognition Using Visual Context](https://mlsanity.com/paper/2005.00589/) (2020) - [Complicated Table Structure Recognition](https://mlsanity.com/paper/1908.04729/) (2019) - [TableNet: Deep Learning model for end-to-end Table detection and Tabular data extraction from Scanned Document Images](https://mlsanity.com/paper/2001.01469/) (2020) - [MASTER: Multi-Aspect Non-local Network for Scene Text Recognition](https://mlsanity.com/paper/1910.02562/) (2019) - [Table Structure Recognition using Top-Down and Bottom-Up Cues](https://mlsanity.com/paper/2010.04565/) (2020) - [LGPMA: Complicated Table Structure Recognition with Local and Global Pyramid Mask Alignment](https://mlsanity.com/paper/2105.06224/) (2021) - [Parsing Table Structures in the Wild](https://mlsanity.com/paper/2109.02199/) (2021) - [Split, embed and merge: An accurate table structure recognizer](https://mlsanity.com/paper/2107.05214/) (2021) - [Neural Collaborative Graph Machines for Table Structure Recognition](https://mlsanity.com/paper/2111.13359/) (2021) - [Current Status and Performance Analysis of Table Recognition in Document Images with Deep Neural Networks](https://mlsanity.com/paper/2104.14272/) (2021) ## Built on by (most cited first, 19 of 19) - [OmniParser: A Unified Framework for Text Spotting, Key Information Extraction and Table Recognition](https://mlsanity.com/paper/2403.19128/) (2024) - [TabPedia: Towards Comprehensive Visual Table Understanding with Concept Synergy](https://mlsanity.com/paper/2406.01326/) (2024) - [Document Parsing Unveiled: Techniques, Challenges, and Prospects for Structured Information Extraction](https://mlsanity.com/paper/2410.21169/) (2024) - [Improving Table Structure Recognition with Visual-Alignment Sequential Coordinate Modeling](https://mlsanity.com/paper/2303.06949/) (2023) - [OmniParser V2: Structured-Points-of-Thought for Unified Visual Text Parsing and Its Generality to Multimodal Large Language Models](https://mlsanity.com/paper/2502.16161/) (2025) - [LLM/Agent-as-Data-Analyst: A Survey](https://mlsanity.com/paper/2509.23988/) (2025) - [GridFormer: Towards Accurate Table Structure Recognition via Grid Prediction](https://mlsanity.com/paper/2309.14962/) (2023) - [Robust Table Structure Recognition with Dynamic Queries Enhanced Detection Transformer](https://mlsanity.com/paper/2303.11615/) (2023) - [UniTabNet: Bridging Vision and Language Models for Enhanced Table Structure Recognition](https://mlsanity.com/paper/2409.13148/) (2024) - [TFLOP: Table Structure Recognition Framework with Layout Pointer Mechanism](https://mlsanity.com/paper/2501.11800/) (2025) - [Table2LaTeX-RL: High-Fidelity LaTeX Code Generation from Table Images via Reinforced Multimodal Language Models](https://mlsanity.com/paper/2509.17589/) (2025) - [Grab What You Need: Rethinking Complex Table Structure Recognition with Flexible Components Deliberation](https://mlsanity.com/paper/2303.09174/) (2023) - [TABLET: Table Structure Recognition using Encoder-only Transformers](https://mlsanity.com/paper/2506.07015/) (2025) - [Spatial ModernBERT: Spatial-Aware Transformer for Table and Key-Value Extraction in Financial Documents at Scale](https://mlsanity.com/paper/2507.08865/) (2025) - [SepFormer: Coarse-to-fine Separator Regression Network for Table Structure Recognition](https://mlsanity.com/paper/2506.21920/) (2025) - [TableSeq: Unified Generation of Structure, Content, and Layout](https://mlsanity.com/paper/2604.16070/) (2026) - [FastTab: A Fast Table Recognizer with a Tiny Recursive Module and 1D Transformers](https://mlsanity.com/paper/2605.22422/) (2026) - [From Diagnosis to Correction: Benchmarking and Improving Real-World Table Parsing](https://mlsanity.com/paper/2608.09842/) (2026) - [Enhancing Table Structure Recognition via Bounding Box Guidance](https://mlsanity.com/paper/2609.08705/) (2026) ## Abstract (arXiv) Table structure recognition is a crucial part of document image analysis domain. Its difficulty lies in the need to parse the physical coordinates and logical indices of each cell at the same time. However, the existing methods are difficult to achieve both these goals, especially when the table splitting lines are blurred or tilted. In this paper, we propose an accurate and end-to-end transformer-based table structure recognition method, referred to as TRUST. Transformers are suitable for table structure recognition because of their global computations, perfect memory, and parallel computation. By introducing novel Transformer-based Query-based Splitting Module and Vertex-based Merging Module, the table structure recognition problem is decoupled into two joint optimization sub-tasks: multi-oriented table row/column splitting and table grid merging. The Query-based Splitting Module learns strong context information from long dependencies via Transformer networks, accurately predicts the multi-oriented table row/column separators, and obtains the basic grids of the table accordingly. The Vertex-based Merging Module is capable of aggregating local contextual information between adjacent basic grids, providing the ability to merge basic girds that belong to the same spanning cell accurately. We conduct experiments on several popular benchmarks including PubTabNet and SynthTable, our method achieves new state-of-the-art results. In particular, TRUST runs at 10 FPS on PubTabNet, surpassing the previous methods by a large margin.