# Key Information Extraction From Documents: Evaluation And Generator

arXiv: https://arxiv.org/abs/2106.14624 · HTML: https://mlsanity.com/paper/2106.14624/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 18 references that are in the map.
- First posted on arXiv 9 Jun 2021.

## Builds on (most cited first, 8 of 8)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019)
- [Know What You Don't Know: Unanswerable Questions for SQuAD](https://mlsanity.com/paper/1806.03822/) (2018)
- [Empirical Evaluation of Rectified Activations in Convolutional Network](https://mlsanity.com/paper/1505.00853/) (2015)
- [ICDAR2019 Competition on Scanned Receipt OCR and Information Extraction](https://mlsanity.com/paper/2103.10213/) (2021)
- [Chargrid: Towards Understanding 2D Documents](https://mlsanity.com/paper/1809.08799/) (2018)
- [BERTgrid: Contextualized Embedding for 2D Document Representation and Understanding](https://mlsanity.com/paper/1909.04948/) (2019)
- [CUTIE: Learning to Understand Documents with Convolutional Universal Text Information Extractor](https://mlsanity.com/paper/1903.12363/) (2019)

## Built on by (most cited first, 7 of 7)

- [DocILE Benchmark for Document Information Localization and Extraction](https://mlsanity.com/paper/2302.05658/) (2023)
- [Business Document Information Extraction: Towards Practical Benchmarks](https://mlsanity.com/paper/2206.11229/) (2022)
- [Beyond Document Page Classification: Design, Datasets, and Challenges](https://mlsanity.com/paper/2308.12896/) (2023)
- [Optimizing RAG Techniques for Automotive Industry PDF Chatbots: A Case Study with Locally Deployed Ollama Models](https://mlsanity.com/paper/2408.05933/) (2024)
- [DocILE 2023 Teaser: Document Information Localization and Extraction](https://mlsanity.com/paper/2301.12394/) (2023)
- [Enhancing Large Multimodal Models in Key Information Extraction via Scene-Aware Document Synthesis](https://mlsanity.com/paper/2607.04636/) (2026)
- [DocAnnot -- Accelerating the Creation of Key Information Extraction Datasets with GenAI-Powered Auto-annotation](https://mlsanity.com/paper/2607.24745/) (2026)

## Abstract (arXiv)

Extracting information from documents usually relies on natural language processing methods working on one-dimensional sequences of text. In some cases, for example, for the extraction of key information from semi-structured documents, such as invoice-documents, spatial and formatting information of text are crucial to understand the contextual meaning. Convolutional neural networks are already common in computer vision models to process and extract relationships in multidimensional data. Therefore, natural language processing models have already been combined with computer vision models in the past, to benefit from e.g. positional information and to improve performance of these key information extraction models. Existing models were either trained on unpublished data sets or on an annotated collection of receipts, which did not focus on PDF-like documents. Hence, in this research project a template-based document generator was created to compare state-of-the-art models for information extraction. An existing information extraction model "Chargrid" (Katti et al., 2019) was reconstructed and the impact of a bounding box regression decoder, as well as the impact of an NLP pre-processing step was evaluated for information extraction from documents. The results have shown that NLP based pre-processing is beneficial for model performance. However, the use of a bounding box regression decoder increases the model performance only for fields that do not follow a rectangular shape.