mlsanityData to 7 Oct 2026

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

arXiv 2408.06345 ↗First posted 23 Jul 2024cs.IR cs.CL cs.LGcited by 6

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Abstract (arXiv)

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.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 65 of its 133 references that are in the map. First posted on arXiv 23 Jul 2024.

Builds on

65 of 133 references.

References · 65

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38 more
  1. 2021ICDAR2019 Competition on Scanned Receipt OCR and Information Extractioncited by 254
  2. 2021DocFormer: End-to-End Transformer for Document Understandingcited by 254
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  14. 2022FormNet: Structural Encoding beyond Sequential Modeling in Form Document Information Extractioncited by 64
  15. 2019BERTgrid: Contextualized Embedding for 2D Document Representation and Understandingcited by 63
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  37. 2021Tag, Copy or Predict: A Unified Weakly-Supervised Learning Framework for Visual Information Extraction using Sequencescited by 8
  38. 2021A Span Extraction Approach for Information Extraction on Visually-Rich Documentscited by 6

50 most cited of 65 All 65 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 5.

6 papers.

  1. 2025ALDEN: Reinforcement Learning for Active Navigation and Evidence Gathering in Long Documentscited by 2
  2. 2026Tabular PDF Information Extraction with Local LLMs and Layout-Aware Parsing: A Reliability Evaluationcited by 0
  3. 2026Material Database Agent: A Multimodal Agentic Framework for Scientific Literature Miningcited by 0
  4. 2026Multimodal Approaches for Visually-Rich Document Type Classification: A Comparative Analysiscited by 0
  5. 2026OCR-Based Field Extraction for Archaeological Pottery Metadata: The CENTURIA Datasetcited by 0
  6. 2026AAS-RAIL: Improving Information Extraction for Asset Administration Shells through Retrieval-Augmented In-Context Learningcited by 0