{
  "id": "2001.05853",
  "version": "v1",
  "published": "2020-01-13T20:42:40.000Z",
  "updated": "2020-01-13T20:42:40.000Z",
  "title": "Identifying Table Structure in Documents using Conditional Generative Adversarial Networks",
  "authors": [
    "Nataliya Le Vine",
    "Claus Horn",
    "Matthew Zeigenfuse",
    "Mark Rowan"
  ],
  "comment": "arXiv admin note: substantial text overlap with arXiv:1904.01947",
  "categories": [
    "cs.CV",
    "cs.NE"
  ],
  "abstract": "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.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2020-01-13T20:42:40.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "conditional generative adversarial network",
      "identifying table structure",
      "form denoting approximate row",
      "small data set sizes"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}