# Table understanding in structured documents

arXiv: https://arxiv.org/abs/1904.12577 · HTML: https://mlsanity.com/paper/1904.12577/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Mar 2019.

## Built on by (most cited first, 11 of 11)

- [TabPedia: Towards Comprehensive Visual Table Understanding with Concept Synergy](https://mlsanity.com/paper/2406.01326/) (2024)
- [DocILE Benchmark for Document Information Localization and Extraction](https://mlsanity.com/paper/2302.05658/) (2023)
- [Robust Table Detection and Structure Recognition from Heterogeneous Document Images](https://mlsanity.com/paper/2203.09056/) (2022)
- [Current Status and Performance Analysis of Table Recognition in Document Images with Deep Neural Networks](https://mlsanity.com/paper/2104.14272/) (2021)
- [Deep learning for table detection and structure recognition: A survey](https://mlsanity.com/paper/2211.08469/) (2022)
- [Business Document Information Extraction: Towards Practical Benchmarks](https://mlsanity.com/paper/2206.11229/) (2022)
- [A Graphical Approach to Document Layout Analysis](https://mlsanity.com/paper/2308.02051/) (2023)
- [Graph Neural Networks and Representation Embedding for Table Extraction in PDF Documents](https://mlsanity.com/paper/2208.11203/) (2022)
- [TC-OCR: TableCraft OCR for Efficient Detection & Recognition of Table Structure & Content](https://mlsanity.com/paper/2404.10305/) (2024)
- [DocILE 2023 Teaser: Document Information Localization and Extraction](https://mlsanity.com/paper/2301.12394/) (2023)
- [TDeLTA: A Light-weight and Robust Table Detection Method based on Learning Text Arrangement](https://mlsanity.com/paper/2312.11043/) (2023)

## Abstract (arXiv)

Abstract--- Table detection and extraction has been studied in the context of documents like reports, where tables are clearly outlined and stand out from the document structure visually. We study this topic in a rather more challenging domain of layout-heavy business documents, particularly invoices. Invoices present the novel challenges of tables being often without outlines - either in the form of borders or surrounding text flow - with ragged columns and widely varying data content. We will also show, that we can extract specific information from structurally different tables or table-like structures with one model. We present a comprehensive representation of a page using graph over word boxes, positional embeddings, trainable textual features and rephrase the table detection as a text box labeling problem. We will work on our newly presented dataset of pro forma invoices, invoices and debit note documents using this representation and propose multiple baselines to solve this labeling problem. We then propose a novel neural network model that achieves strong, practical results on the presented dataset and analyze the model performance and effects of graph convolutions and self-attention in detail.