# Extracting Tables from Documents using Conditional Generative Adversarial Networks and Genetic Algorithms

arXiv: https://arxiv.org/abs/1904.01947 · HTML: https://mlsanity.com/paper/1904.01947/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Apr 2019.

## Built on by (most cited first, 3 of 3)

- [Visual Understanding of Complex Table Structures from Document Images](https://mlsanity.com/paper/2111.07129/) (2021)
- [Tab2Know: Building a Knowledge Base from Tables in Scientific Papers](https://mlsanity.com/paper/2107.13306/) (2021)
- [RAPTOR: Refined Approach for Product Table Object Recognition](https://mlsanity.com/paper/2502.14918/) (2025)

## Abstract (arXiv)

Extracting information from tables in documents presents a significant challenge in many industries and in academic research. Existing methods which take a bottom-up approach of integrating lines into cells and rows or columns neglect the available prior information relating to table structure. Our proposed method takes a top-down approach, first using a generative adversarial network to map a table image into a standardised `skeleton' table form denoting the approximate row and column borders without table content, then fitting renderings of candidate latent table structures to the skeleton structure using a distance measure optimised by a genetic algorithm.