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    <titleInfo>
        <title>Extracting structured data from invoices</title>
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        <namePart type="given">Xavier</namePart>
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            <namePart type="given">Sunghwan</namePart>
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            <namePart type="given">Xiuzhen</namePart>
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    <abstract>Business documents encode a wealth of information in a format tailored to human consumption – i.e. aesthetically disbursed natural language text, graphics and tables. We address the task of extracting key fields (e.g. the amount due on an invoice) from a wide-variety of potentially unseen document formats. In contrast to traditional template driven extraction systems, we introduce a content-driven machine-learning approach which is both robust to noise and generalises to unseen document formats. In a comparison of our approach with alternative invoice extraction systems, we observe an absolute accuracy gain of 20\textbackslash% across compared fields, and a 25\textbackslash%–94\textbackslash% reduction in extraction latency.</abstract>
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%0 Conference Proceedings
%T Extracting structured data from invoices
%A Holt, Xavier
%A Chisholm, Andrew
%Y Kim, Sunghwan Mac
%Y Zhang, Xiuzhen (Jenny)
%S Proceedings of the Australasian Language Technology Association Workshop 2018
%D 2018
%8 December
%C Dunedin, New Zealand
%F holt-chisholm-2018-extracting
%X Business documents encode a wealth of information in a format tailored to human consumption – i.e. aesthetically disbursed natural language text, graphics and tables. We address the task of extracting key fields (e.g. the amount due on an invoice) from a wide-variety of potentially unseen document formats. In contrast to traditional template driven extraction systems, we introduce a content-driven machine-learning approach which is both robust to noise and generalises to unseen document formats. In a comparison of our approach with alternative invoice extraction systems, we observe an absolute accuracy gain of 20\textbackslash% across compared fields, and a 25\textbackslash%–94\textbackslash% reduction in extraction latency.
%U https://aclanthology.org/U18-1006
%P 53-59

Markdown (Informal)

[Extracting structured data from invoices](https://aclanthology.org/U18-1006) (Holt & Chisholm, ALTA 2018)

• Extracting structured data from invoices (Holt & Chisholm, ALTA 2018)

ACL

• Xavier Holt and Andrew Chisholm. 2018. Extracting structured data from invoices. In Proceedings of the Australasian Language Technology Association Workshop 2018, pages 53–59, Dunedin, New Zealand.