TableParser: Automatic Table Parsing with Weak Supervision from Spreadsheets

• URL: http://arxiv.org/abs/2201.01654v1
• Date: Wed, 5 Jan 2022 15:21:06 GMT
• Title: TableParser: Automatic Table Parsing with Weak Supervision from Spreadsheets
• Authors: Susie Xi Rao, Johannes Rausch, Peter Egger, Ce Zhang
• Abstract summary: We devise a system capable of parsing tables in both native PDFs and scanned images with high precision. We also create TableAnnotator and ExcelAnnotator, which constitute a spreadsheet-based weak supervision mechanism.
• Score: 5.5347995556789105
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Tables have been an ever-existing structure to store data. There exist now different approaches to store tabular data physically. PDFs, images, spreadsheets, and CSVs are leading examples. Being able to parse table structures and extract content bounded by these structures is of high importance in many applications. In this paper, we devise TableParser, a system capable of parsing tables in both native PDFs and scanned images with high precision. We have conducted extensive experiments to show the efficacy of domain adaptation in developing such a tool. Moreover, we create TableAnnotator and ExcelAnnotator, which constitute a spreadsheet-based weak supervision mechanism and a pipeline to enable table parsing. We share these resources with the research community to facilitate further research in this interesting direction.

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