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

Nataliya Le Vine; Matthew Zeigenfuse; Mark Rowan

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arXiv · 1904.01947

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

Nataliya Le Vine·Matthew Zeigenfuse·Mark Rowan

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Abstract

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.

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Nataliya Le Vine, Matthew Zeigenfuse, Mark Rowan. 2019-04-03. Extracting Tables from Documents using Conditional Generative Adversarial Networks and Genetic Algorithms. https://arxiv.org/abs/1904.01947

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