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.

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

References in corpus (9)

• A Survey on Deep Learning for Named Entity Recognition
• ICDAR2019 Competition on Scanned Receipt OCR and Information Extraction
• BERTgrid: Contextualized Embedding for 2D Document Representation and Understanding
• A Survey of Deep Learning Approaches for OCR and Document Understanding
• VRDU: A Benchmark for Visually-rich Document Understanding
• Improving Information Extraction on Business Documents with Specific Pre-Training Tasks
• An Information Extraction Study: Take In Mind the Tokenization!
• "What is the value of {templates}?" Rethinking Document Information Extraction Datasets for LLMs
• Transformers and Language Models in Form Understanding: A Comprehensive Review of Scanned Document Analysis