Paper ID: 2205.11215

Document Intelligence Metrics for Visually Rich Document Evaluation

Jonathan DeGange, Swapnil Gupta, Zhuoyu Han, Krzysztof Wilkosz, Adam Karwan

The processing of Visually-Rich Documents (VRDs) is highly important in information extraction tasks associated with Document Intelligence. We introduce DI-Metrics, a Python library devoted to VRD model evaluation comprising text-based, geometric-based and hierarchical metrics for information extraction tasks. We apply DI-Metrics to evaluate information extraction performance using publicly available CORD dataset, comparing performance of three SOTA models and one industry model. The open-source library is available on GitHub.

Submitted: May 23, 2022