Deep Learning based Key Information Extraction from Business Documents: Systematic Literature Review

Alexander Michael Rombach; Peter Fettke

Deep Learning based Key Information Extraction from Business Documents: Systematic Literature Review

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arxiv 2408.06345 v2 pith:OZBTH76N submitted 2024-07-23 cs.IR cs.CLcs.LG

Alexander Michael Rombach , Peter Fettke This is my paper · ORCID

classification cs.IRcs.CLcs.LG

keywords approachesbusinessdeepdocumentsinformationlearningextractionliterature

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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.

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VDInstruct: Zero-Shot Key Information Extraction via Content-Aware Vision Tokenization
cs.CV 2025-07 conditional novelty 6.0 of 10

VDInstruct achieves strong zero-shot key-information extraction by combining a region detector with content-aware vision tokenization, using about 500 image tokens per page.

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