DECO: A Dataset of Annotated Spreadsheets for Layout and Table Recognition This paper presents DECO (Dresden Enron COrpus), a dataset of spreadsheet files, annotated on the basis of layout and contents. It comprises of 1,165 files, extracted from the Enron corpus. Three different annotators (judges) assigned layout roles (e.g., Header, Data, and Notes) to nonempty cells and marked the borders of tables. Files that do not contain tables were flagged using categories such as Template, Form, and Report. Subsequently, a thorough analysis is performed to uncover the characteristics of the overall dataset and specific annotations. The results are discussed in this paper, providing several takeaways for future works. Furthermore, this work describes in detail the annotation methodology, going through the individual steps. The dataset, methodology, and tools are made publicly available, so that they can be adopted for further studies. DECO is available at: https:wwwdb.inf.tudresden.deresearchprojectsdeexcelarator

A GeneticBased Search for Adaptive Table Recognition in Spreadsheets Spreadsheets are very successful content generation tools, used in almost every enterprise to create a wealth of information. However, this information is often intermingled with various formatting, layout, and textual metadata, making it hard to identify and interpret the tabular payload. Previous works proposed to solve this problem by mainly using heuristics. Although fast to implement, these approaches fail to capture the high variability of usergenerated spreadsheet tables. Therefore, in this paper, we propose a supervised approach that is able to adapt to arbitrary spreadsheet datasets. We use a graph model to represent the contents of a sheet, which carries layout and spatial features. Subsequently, we apply geneticbased approaches for graph partitioning, to recognize the parts of the graph corresponding to tables in the sheet. The search for tables is guided by an objective function, which is tuned to match the specific characteristics of a given dataset. We present the feasibility of this approach with an experimental evaluation, on a large, realworld spreadsheet corpus.