TableParser: Automatic Table Parsing with Weak Supervision from Spreadsheets
This work addresses the need for accurate table extraction in applications like data analysis, though it appears incremental by building on existing parsing methods with new tools.
The authors tackled the problem of parsing tables from PDFs and scanned images by developing TableParser, a system that achieved high precision through domain adaptation and a weak supervision mechanism using spreadsheets.
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.