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- sl:arxiv_author :
- sl:arxiv_firstAuthor : Nataliya Le Vine
- sl:arxiv_num : 1904.01947
- sl:arxiv_published : 2019-04-03T12:12:03Z
- sl:arxiv_summary : 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.@en
- sl:arxiv_title : Extracting Tables from Documents using Conditional Generative Adversarial Networks and Genetic Algorithms@en
- sl:arxiv_updated : 2019-04-03T12:12:03Z
- sl:bookmarkOf : https://arxiv.org/abs/1904.01947
- sl:creationDate : 2020-04-02
- sl:creationTime : 2020-04-02T15:48:47Z
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