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arXiv · 2205.11215

Document Intelligence Metrics for Visually Rich Document Evaluation

Jonathan DeGange·Swapnil Gupta·Zhuoyu Han·Krzysztof Wilkosz·Adam Karwan
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Abstract

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.

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Jonathan DeGange, Swapnil Gupta, Zhuoyu Han, Krzysztof Wilkosz, Adam Karwan. 2022-05-23. Document Intelligence Metrics for Visually Rich Document Evaluation. https://arxiv.org/abs/2205.11215

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