# Table Structure Extraction with Bi-directional Gated Recurrent Unit Networks

arXiv: https://arxiv.org/abs/2001.02501 · HTML: https://mlsanity.com/paper/2001.02501/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jan 2020.

## Built on by (most cited first, 20 of 20)

- [LGPMA: Complicated Table Structure Recognition with Local and Global Pyramid Mask Alignment](https://mlsanity.com/paper/2105.06224/) (2021)
- [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)
- [Neural Collaborative Graph Machines for Table Structure Recognition](https://mlsanity.com/paper/2111.13359/) (2021)
- [Robust Table Detection and Structure Recognition from Heterogeneous Document Images](https://mlsanity.com/paper/2203.09056/) (2022)
- [TGRNet: A Table Graph Reconstruction Network for Table Structure Recognition](https://mlsanity.com/paper/2106.10598/) (2021)
- [TSRFormer: Table Structure Recognition with Transformers](https://mlsanity.com/paper/2208.04921/) (2022)
- [Current Status and Performance Analysis of Table Recognition in Document Images with Deep Neural Networks](https://mlsanity.com/paper/2104.14272/) (2021)
- [Visual Understanding of Complex Table Structures from Document Images](https://mlsanity.com/paper/2111.07129/) (2021)
- [Guided Table Structure Recognition through Anchor Optimization](https://mlsanity.com/paper/2104.10538/) (2021)
- [Deep learning for table detection and structure recognition: A survey](https://mlsanity.com/paper/2211.08469/) (2022)
- [Robust Table Structure Recognition with Dynamic Queries Enhanced Detection Transformer](https://mlsanity.com/paper/2303.11615/) (2023)
- [Grab What You Need: Rethinking Complex Table Structure Recognition with Flexible Components Deliberation](https://mlsanity.com/paper/2303.09174/) (2023)
- [TRACE: Table Reconstruction Aligned to Corner and Edges](https://mlsanity.com/paper/2305.00630/) (2023)
- [TABLET: Table Structure Recognition using Encoder-only Transformers](https://mlsanity.com/paper/2506.07015/) (2025)
- [A Study on Reproducibility and Replicability of Table Structure Recognition Methods](https://mlsanity.com/paper/2304.10439/) (2023)
- [DavarOCR: A Toolbox for OCR and Multi-Modal Document Understanding](https://mlsanity.com/paper/2207.06695/) (2022)
- [Uncertainty Quantification in Table Structure Recognition](https://mlsanity.com/paper/2407.01731/) (2024)
- [TableCenterNet: A one-stage network for table structure recognition](https://mlsanity.com/paper/2504.17522/) (2025)
- [SepFormer: Coarse-to-fine Separator Regression Network for Table Structure Recognition](https://mlsanity.com/paper/2506.21920/) (2025)

## Abstract (arXiv)

Tables present summarized and structured information to the reader, which makes table structure extraction an important part of document understanding applications. However, table structure identification is a hard problem not only because of the large variation in the table layouts and styles, but also owing to the variations in the page layouts and the noise contamination levels. A lot of research has been done to identify table structure, most of which is based on applying heuristics with the aid of optical character recognition (OCR) to hand pick layout features of the tables. These methods fail to generalize well because of the variations in the table layouts and the errors generated by OCR. In this paper, we have proposed a robust deep learning based approach to extract rows and columns from a detected table in document images with a high precision. In the proposed solution, the table images are first pre-processed and then fed to a bi-directional Recurrent Neural Network with Gated Recurrent Units (GRU) followed by a fully-connected layer with soft max activation. The network scans the images from top-to-bottom as well as left-to-right and classifies each input as either a row-separator or a column-separator. We have benchmarked our system on publicly available UNLV as well as ICDAR 2013 datasets on which it outperformed the state-of-the-art table structure extraction systems by a significant margin.