Identifying Table Structure in Documents using Conditional Generative Adversarial Networks

In many industries, as well as in academic research, information is primarily transmitted in the form of unstructured documents (this article, for example). Hierarchically-related data is rendered as tables, and extracting information from tables in such documents presents a significant challenge. Many existing methods take a bottom-up approach, first integrating lines into cells, then cells into rows or columns, and finally inferring a structure from the resulting 2-D layout. But such approaches neglect the available prior information relating to table structure, namely that the table is merely an arbitrary representation of a latent logical structure. We propose a top-down approach, first using a conditional generative adversarial network to map a table image into a standardised `skeleton' table form denoting approximate row and column borders without table content, then deriving latent table structure using xy-cut projection and Genetic Algorithm optimisation. The approach is easily adaptable to different table configurations and requires small data set sizes for training.

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

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.