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：

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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。