Extracting structured data from invoices
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.
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