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.
@article{arxiv.1904.01947,
title = {Extracting Tables from Documents using Conditional Generative Adversarial Networks and Genetic Algorithms},
author = {Nataliya Le Vine and Matthew Zeigenfuse and Mark Rowan},
journal= {arXiv preprint arXiv:1904.01947},
year = {2019}
}