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- W2925702600 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." @default.
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- W2925702600 date "2019-04-03" @default.
- W2925702600 modified "2023-09-25" @default.
- W2925702600 title "Extracting Tables from Documents using Conditional Generative Adversarial Networks and Genetic Algorithms" @default.
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