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\% across compared fields, and a 25\%–94\% reduction in extraction latency.- Anthology ID:
- U18-1006
- Volume:
- Proceedings of the Australasian Language Technology Association Workshop 2018
- Month:
- December
- Year:
- 2018
- Address:
- Dunedin, New Zealand
- Editors:
- Sunghwan Mac Kim, Xiuzhen (Jenny) Zhang
- Venue:
- ALTA
- SIG:
- Publisher:
- Note:
- Pages:
- 53–59
- Language:
- URL:
- https://aclanthology.org/U18-1006
- DOI:
- Cite (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.
- Cite (Informal):
- Extracting structured data from invoices (Holt & Chisholm, ALTA 2018)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/U18-1006.pdf