Document Intelligence Metrics for Visually Rich Document Evaluation

• URL: http://arxiv.org/abs/2205.11215v1
• Date: Mon, 23 May 2022 11:55:05 GMT
• Title: Document Intelligence Metrics for Visually Rich Document Evaluation
• Authors: Jonathan DeGange, Swapnil Gupta, Zhuoyu Han, Krzysztof Wilkosz, Adam Karwan
• Abstract summary: We introduce DI-Metrics, a Python library devoted to VRD model evaluation. We apply DI-Metrics to evaluate information extraction performance using publicly available CORD dataset.
• Score: 0.10499611180329803
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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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