# LORE++: Logical Location Regression Network for Table Structure Recognition with Pre-training

arXiv: https://arxiv.org/abs/2401.01522 · HTML: https://mlsanity.com/paper/2401.01522/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 41 references that are in the map.
- First posted on arXiv 3 Jan 2024.

## Builds on (most cited first, 30 of 31)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [Objects as Points](https://mlsanity.com/paper/1904.07850/) (2019)
- [LayoutLM: Pre-training of Text and Layout for Document Image Understanding](https://mlsanity.com/paper/1912.13318/) (2019)
- [GroupViT: Semantic Segmentation Emerges from Text Supervision](https://mlsanity.com/paper/2202.11094/) (2022)
- [Deep Layer Aggregation](https://mlsanity.com/paper/1707.06484/) (2017)
- [LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding](https://mlsanity.com/paper/2012.14740/) (2020)
- [Open-Vocabulary Panoptic Segmentation with Text-to-Image Diffusion Models](https://mlsanity.com/paper/2303.04803/) (2023)
- [Image-based table recognition: data, model, and evaluation](https://mlsanity.com/paper/1911.10683/) (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)
- [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)
- [CascadeTabNet: An approach for end to end table detection and structure recognition from image-based documents](https://mlsanity.com/paper/2004.12629/) (2020)
- [Complicated Table Structure Recognition](https://mlsanity.com/paper/1908.04729/) (2019)
- [DETReg: Unsupervised Pretraining with Region Priors for Object Detection](https://mlsanity.com/paper/2106.04550/) (2021)
- [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)
- [Towards Robust Visual Information Extraction in Real World: New Dataset and Novel Solution](https://mlsanity.com/paper/2102.06732/) (2021)
- [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)
- [GeoLayoutLM: Geometric Pre-training for Visual Information Extraction](https://mlsanity.com/paper/2304.10759/) (2023)
- [PingAn-VCGroup's Solution for ICDAR 2021 Competition on Scientific Literature Parsing Task B: Table Recognition to HTML](https://mlsanity.com/paper/2105.01848/) (2021)
- [Neural Collaborative Graph Machines for Table Structure Recognition](https://mlsanity.com/paper/2111.13359/) (2021)
- [TGRNet: A Table Graph Reconstruction Network for Table Structure Recognition](https://mlsanity.com/paper/2106.10598/) (2021)
- [Modeling Entities as Semantic Points for Visual Information Extraction in the Wild](https://mlsanity.com/paper/2303.13095/) (2023)
- [LORE: Logical Location Regression Network for Table Structure Recognition](https://mlsanity.com/paper/2303.03730/) (2023)
- [Visual Understanding of Complex Table Structures from Document Images](https://mlsanity.com/paper/2111.07129/) (2021)

## Built on by (most cited first, 8 of 8)

- [MindVL: Towards Efficient and Effective Training of Multimodal Large Language Models on Ascend NPUs](https://mlsanity.com/paper/2509.11662/) (2025)
- [TableCenterNet: A one-stage network for table structure recognition](https://mlsanity.com/paper/2504.17522/) (2025)
- [InstructTable: Improving Table Structure Recognition Through Instructions](https://mlsanity.com/paper/2604.02880/) (2026)
- [HIP: Hierarchical Point Modeling and Pre-training for Visual Information Extraction](https://mlsanity.com/paper/2411.01139/) (2024)
- [SepFormer: Coarse-to-fine Separator Regression Network for Table Structure Recognition](https://mlsanity.com/paper/2506.21920/) (2025)
- [FastTab: A Fast Table Recognizer with a Tiny Recursive Module and 1D Transformers](https://mlsanity.com/paper/2605.22422/) (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)

## Abstract (arXiv)

Table structure recognition (TSR) aims at extracting tables in images into machine-understandable formats. Recent methods solve this problem by predicting the adjacency relations of detected cell boxes or learning to directly generate the corresponding markup sequences from the table images. However, existing approaches either count on additional heuristic rules to recover the table structures, or face challenges in capturing long-range dependencies within tables, resulting in increased complexity. In this paper, we propose an alternative paradigm. We model TSR as a logical location regression problem and propose a new TSR framework called LORE, standing for LOgical location REgression network, which for the first time regresses logical location as well as spatial location of table cells in a unified network. Our proposed LORE is conceptually simpler, easier to train, and more accurate than other paradigms of TSR. Moreover, inspired by the persuasive success of pre-trained models on a number of computer vision and natural language processing tasks, we propose two pre-training tasks to enrich the spatial and logical representations at the feature level of LORE, resulting in an upgraded version called LORE++. The incorporation of pre-training in LORE++ has proven to enjoy significant advantages, leading to a substantial enhancement in terms of accuracy, generalization, and few-shot capability compared to its predecessor. Experiments on standard benchmarks against methods of previous paradigms demonstrate the superiority of LORE++, which highlights the potential and promising prospect of the logical location regression paradigm for TSR.