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

arXiv: https://arxiv.org/abs/2208.04011 · HTML: https://mlsanity.com/paper/2208.04011/

## 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 (most cited first, 12 of 12)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [LayoutLM: Pre-training of Text and Layout for Document Image Understanding](https://mlsanity.com/paper/1912.13318/) (2019)
- [LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding](https://mlsanity.com/paper/2012.14740/) (2020)
- [FUNSD: A Dataset for Form Understanding in Noisy Scanned Documents](https://mlsanity.com/paper/1905.13538/) (2019)
- [Evaluation of Deep Convolutional Nets for Document Image Classification and Retrieval](https://mlsanity.com/paper/1502.07058/) (2015)
- [Graph Convolution for Multimodal Information Extraction from Visually Rich Documents](https://mlsanity.com/paper/1903.11279/) (2019)
- [Kleister: Key Information Extraction Datasets Involving Long Documents with Complex Layouts](https://mlsanity.com/paper/2105.05796/) (2021)
- [PICK: Processing Key Information Extraction from Documents using Improved Graph Learning-Convolutional Networks](https://mlsanity.com/paper/2004.07464/) (2020)
- [CloudScan - A configuration-free invoice analysis system using recurrent neural networks](https://mlsanity.com/paper/1708.07403/) (2017)
- [Attend, Copy, Parse -- End-to-end information extraction from documents](https://mlsanity.com/paper/1812.07248/) (2018)
- [CUTIE: Learning to Understand Documents with Convolutional Universal Text Information Extractor](https://mlsanity.com/paper/1903.12363/) (2019)
- [First Steps Toward CNN based Source Classification of Document Images Shared Over Messaging App](https://mlsanity.com/paper/1808.05941/) (2018)

## Built on by (most cited first, 5 of 5)

- [LLM/Agent-as-Data-Analyst: A Survey](https://mlsanity.com/paper/2509.23988/) (2025)
- [Towards reducing hallucination in extracting information from financial reports using Large Language Models](https://mlsanity.com/paper/2310.10760/) (2023)
- [IndicDLP: A Foundational Dataset for Multi-Lingual and Multi-Domain Document Layout Parsing](https://mlsanity.com/paper/2512.20236/) (2025)
- [An Agentic Framework Using Rules and LLMs for Embedding and Annotating Descriptive Document Layouts: A Plant Science Use Case](https://mlsanity.com/paper/2608.14587/) (2026)
- [Systematic Literature Review of Machine Learning Models and Applications for Text Recognition](https://mlsanity.com/paper/2608.26500/) (2026)

## 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.