Abstract
Tables in documents are a rich and under-exploited source of structured data in otherwise unstructured documents. The extraction and understanding of tabular data is a challenging task which has attracted the attention of researchers from a range of disciplines such as information retrieval, machine learning and natural language processing. In this demonstration, we present an end-to-end table extraction and understanding system which takes a PDF file and automatically generates a set of XML and CSV files containing the extracted cells, rows and columns of tables, as well as a complete reading order analysis of the tables. Unlike many systems that work as a black-boxed, ad-hoc solution, our system design incorporates the open, reusable and extensible architecture to support research into, and development of, table-processing systems. During the demo, users will see how our system gradually transforms a PDF document into a set of structured files through a series of processing modules, namely: locating, segmenting and function/structure analysis.
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Notes
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It is generally accepted that tables can be understood if one can detect the hierarchical structure of table headers properly and determine how each table data cell can be uniquely accessed through them.
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From the PDF specification (http://www.adobe.com/devnet/pdf/pdf_reference.html.
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Rastan, R., Paik, HY., Shepherd, J., Ryu, S.H., Beheshti, A. (2018). TEXUS: Table Extraction System for PDF Documents. In: Wang, J., Cong, G., Chen, J., Qi, J. (eds) Databases Theory and Applications. ADC 2018. Lecture Notes in Computer Science(), vol 10837. Springer, Cham. https://doi.org/10.1007/978-3-319-92013-9_30
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DOI: https://doi.org/10.1007/978-3-319-92013-9_30
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