mlsanityData to 14 Oct 2026

Information Extraction from Scanned Invoice Images using Text Analysis and Layout Features

arXiv 2208.04011 ↗First posted 8 Aug 2022cs.CLcited by 5

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Abstract (arXiv)

While storing invoice content as metadata to avoid paper document processing may be the future trend, almost all of daily issued invoices are still printed on paper or generated in digital formats such as PDFs. In this paper, we introduce the OCRMiner system for information extraction from scanned document images which is based on text analysis techniques in combination with layout features to extract indexing metadata of (semi-)structured documents. The system is designed to process the document in a similar way a human reader uses, i.e. to employ different layout and text attributes in a coordinated decision. The system consists of a set of interconnected modules that start with (possibly erroneous) character-based output from a standard OCR system and allow to apply different techniques and to expand the extracted knowledge at each step. Using an open source OCR, the system is able to recover the invoice data in 90% for English and in 88% for the Czech set.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 12 of its 46 references that are in the map. First posted on arXiv 8 Aug 2022.

Builds on

12 of 46 references.

References · 12

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2019LayoutLM: Pre-training of Text and Layout for Document Image Understandingcited by 553
  3. 2020LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understandingcited by 408
  4. 2019FUNSD: A Dataset for Form Understanding in Noisy Scanned Documentscited by 349
  5. 2015Evaluation of Deep Convolutional Nets for Document Image Classification and Retrievalcited by 213
  6. 2019Graph Convolution for Multimodal Information Extraction from Visually Rich Documentscited by 88
  7. 2021Kleister: Key Information Extraction Datasets Involving Long Documents with Complex Layoutscited by 82
  8. 2020PICK: Processing Key Information Extraction from Documents using Improved Graph Learning-Convolutional Networkscited by 72
  9. 2017CloudScan - A configuration-free invoice analysis system using recurrent neural networkscited by 33
  10. 2018Attend, Copy, Parse -- End-to-end information extraction from documentscited by 25
  11. 2019CUTIE: Learning to Understand Documents with Convolutional Universal Text Information Extractorcited by 21
  12. 2018First Steps Toward CNN based Source Classification of Document Images Shared Over Messaging Appcited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 0; 2025: 2; 2026: 2.

5 papers.

  1. 2025LLM/Agent-as-Data-Analyst: A Surveycited by 16
  2. 2023Towards reducing hallucination in extracting information from financial reports using Large Language Modelscited by 11
  3. 2025IndicDLP: A Foundational Dataset for Multi-Lingual and Multi-Domain Document Layout Parsingcited by 1
  4. 2026An Agentic Framework Using Rules and LLMs for Embedding and Annotating Descriptive Document Layouts: A Plant Science Use Casecited by 0
  5. 2026Systematic Literature Review of Machine Learning Models and Applications for Text Recognitioncited by 0