Harvest – a System for Creating Structured Rate Filing Data from Filing PDFs


  • Ender Tekin American Family Insurance
  • Qian You Coupang Inc
  • Devin M. Conathan American Family Insurance
  • Glenn M. Fung American Family Insurance
  • Thomas S. Kneubuehl American Family Insurance




Document Intelligence Document Understanding Machine Learning Convolutional Neural Networks Language Modeling, Deployed System, Transfer Learning


We present a machine-learning-guided process that can efficiently extract factor tables from unstructured rate filing documents. Our approach combines multiple deep-learning-based models that work in tandem to create structured representations of tabular data present in unstructured documents such as pdf files. This process combines CNN's to detect tables, language-based models to extract table metadata and conventional computer vision techniques to improve the accuracy of tabular data on the machine-learning side. The extracted tabular data is validated through an intuitive user interface. This process, which we call Harvest, significantly reduces the time needed to extract tabular information from PDF files, enabling analysis of such data at a speed and scale that was previously unattainable.




How to Cite

Tekin, E., You, Q., Conathan, D. M., Fung, G. M., & Kneubuehl, T. S. (2022). Harvest – a System for Creating Structured Rate Filing Data from Filing PDFs. Proceedings of the AAAI Conference on Artificial Intelligence, 36(11), 12414-12422. https://doi.org/10.1609/aaai.v36i11.21507