# Visual Understanding of Complex Table Structures from Document Images

arXiv: https://arxiv.org/abs/2111.07129 · HTML: https://mlsanity.com/paper/2111.07129/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 42 references that are in the map.
- First posted on arXiv 13 Nov 2021.

## Builds on (most cited first, 22 of 22)

- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Cascade R-CNN: Delving into High Quality Object Detection](https://mlsanity.com/paper/1712.00726/) (2017)
- [EfficientDet: Scalable and Efficient Object Detection](https://mlsanity.com/paper/1911.09070/) (2019)
- [ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks](https://mlsanity.com/paper/1910.03151/) (2019)
- [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)
- [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)
- [Towards Universal Object Detection by Domain Attention](https://mlsanity.com/paper/1904.04402/) (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)
- [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)
- [Split, embed and merge: An accurate table structure recognizer](https://mlsanity.com/paper/2107.05214/) (2021)
- [Table-to-Text: Describing Table Region with Natural Language](https://mlsanity.com/paper/1805.11234/) (2018)
- [Table2Vec: Neural Word and Entity Embeddings for Table Population and Retrieval](https://mlsanity.com/paper/1906.00041/) (2019)
- [Table Structure Extraction with Bi-directional Gated Recurrent Unit Networks](https://mlsanity.com/paper/2001.02501/) (2020)
- [Attention-Guided Version of 2D UNet for Automatic Brain Tumor Segmentation](https://mlsanity.com/paper/2004.02009/) (2020)
- [Exploring Bottom-up and Top-down Cues with Attentive Learning for Webly Supervised Object Detection](https://mlsanity.com/paper/2003.09790/) (2020)
- [Rethinking Table Recognition using Graph Neural Networks](https://mlsanity.com/paper/1905.13391/) (2019)
- [TableLab: An Interactive Table Extraction System with Adaptive Deep Learning](https://mlsanity.com/paper/2102.08445/) (2021)
- [Extracting Tables from Documents using Conditional Generative Adversarial Networks and Genetic Algorithms](https://mlsanity.com/paper/1904.01947/) (2019)

## Built on by (most cited first, 17 of 17)

- [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)
- [TSRFormer: Table Structure Recognition with Transformers](https://mlsanity.com/paper/2208.04921/) (2022)
- [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)
- [LORE: Logical Location Regression Network for Table Structure Recognition](https://mlsanity.com/paper/2303.03730/) (2023)
- [Deep learning for table detection and structure recognition: A survey](https://mlsanity.com/paper/2211.08469/) (2022)
- [VLCDoC: Vision-Language Contrastive Pre-Training Model for Cross-Modal Document Classification](https://mlsanity.com/paper/2205.12029/) (2022)
- [SEMv2: Table Separation Line Detection Based on Instance Segmentation](https://mlsanity.com/paper/2303.04384/) (2023)
- [Robust Table Structure Recognition with Dynamic Queries Enhanced Detection Transformer](https://mlsanity.com/paper/2303.11615/) (2023)
- [LORE++: Logical Location Regression Network for Table Structure Recognition with Pre-training](https://mlsanity.com/paper/2401.01522/) (2024)
- [TableMoE: Neuro-Symbolic Routing for Structured Expert Reasoning in Multimodal Table Understanding](https://mlsanity.com/paper/2506.21393/) (2025)
- [Optimizing RAG Techniques for Automotive Industry PDF Chatbots: A Case Study with Locally Deployed Ollama Models](https://mlsanity.com/paper/2408.05933/) (2024)
- [TableCenterNet: A one-stage network for table structure recognition](https://mlsanity.com/paper/2504.17522/) (2025)
- [CISOL: An Open and Extensible Dataset for Table Structure Recognition in the Construction Industry](https://mlsanity.com/paper/2501.15469/) (2025)
- [TableSeq: Unified Generation of Structure, Content, and Layout](https://mlsanity.com/paper/2604.16070/) (2026)
- [Revisiting Structural Dependency in Autoregressive Multi-Task Table Recognition via Order-Independent Cell-Level Representations](https://mlsanity.com/paper/2606.17874/) (2026)
- [From Diagnosis to Correction: Benchmarking and Improving Real-World Table Parsing](https://mlsanity.com/paper/2608.09842/) (2026)

## Abstract (arXiv)

Table structure recognition is necessary for a comprehensive understanding of documents. Tables in unstructured business documents are tough to parse due to the high diversity of layouts, varying alignments of contents, and the presence of empty cells. The problem is particularly difficult because of challenges in identifying individual cells using visual or linguistic contexts or both. Accurate detection of table cells (including empty cells) simplifies structure extraction and hence, it becomes the prime focus of our work. We propose a novel object-detection-based deep model that captures the inherent alignments of cells within tables and is fine-tuned for fast optimization. Despite accurate detection of cells, recognizing structures for dense tables may still be challenging because of difficulties in capturing long-range row/column dependencies in presence of multi-row/column spanning cells. Therefore, we also aim to improve structure recognition by deducing a novel rectilinear graph-based formulation. From a semantics perspective, we highlight the significance of empty cells in a table. To take these cells into account, we suggest an enhancement to a popular evaluation criterion. Finally, we introduce a modestly sized evaluation dataset with an annotation style inspired by human cognition to encourage new approaches to the problem. Our framework improves the previous state-of-the-art performance by a 2.7% average F1-score on benchmark datasets.