Deep Learning based Key Information Extraction from Business Documents: Systematic Literature Review
Abstract
Extracting key information from documents represents a large portion of business workloads and therefore offers a high potential for efficiency improvements and process automation. With recent advances in Deep Learning, a plethora of Deep Learning based approaches for Key Information Extraction have been proposed under the umbrella term Document Understanding that enable the processing of complex business documents. The goal of this systematic literature review is an in-depth analysis of existing approaches in this domain and the identification of opportunities for further research. To this end, 130 approaches published between 2017 and 2024 are analyzed in this study.
Cite
@article{arxiv.2408.06345,
title = {Deep Learning based Key Information Extraction from Business Documents: Systematic Literature Review},
author = {Alexander Michael Rombach and Peter Fettke},
journal= {arXiv preprint arXiv:2408.06345},
year = {2025}
}
Comments
62 pages, 7 figures, 10 tables; This version represents the accepted author-version without final copyediting. ACM Computing Surveys source: https://dl.acm.org/doi/10.1145/3749369