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Identifying Table Structure in Documents using Conditional Generative Adversarial Networks
arXiv - CS - Neural and Evolutionary Computing Pub Date : 2020-01-13 , DOI: arxiv-2001.05853
Nataliya Le Vine, Claus Horn, Matthew Zeigenfuse, Mark Rowan

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.

中文翻译:

使用条件生成对抗网络识别文档中的表结构

在许多行业以及学术研究中,信息主要以非结构化文档的形式传输(例如本文)。分层相关的数据呈现为表格,从此类文档中的表格中提取信息是一项重大挑战。许多现有方法采用自下而上的方法,首先将线集成到单元中,然后将单元集成到行或列中,最后从生成的二维布局中推断出结构。但是这种方法忽略了与表结构相关的可用先验信息,即表仅仅是潜在逻辑结构的任意表示。我们提出了一种自上而下的方法,首先使用条件生成对抗网络将表格图像映射到标准化的“骨架” 表格形式表示没有表格内容的近似行和列边界,然后使用 xy-cut 投影和遗传算法优化推导出潜在的表格结构。该方法很容易适应不同的表配置,并且需要较小的数据集进行训练。
更新日期:2020-01-17
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