# Doc-GCN: Heterogeneous Graph Convolutional Networks for Document Layout Analysis

arXiv: https://arxiv.org/abs/2208.10970 · HTML: https://mlsanity.com/paper/2208.10970/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 40 references that are in the map.
- First posted on arXiv 22 Aug 2022.

## Builds on (most cited first, 28 of 28)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [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)
- [PubLayNet: largest dataset ever for document layout analysis](https://mlsanity.com/paper/1908.07836/) (2019)
- [FUNSD: A Dataset for Form Understanding in Noisy Scanned Documents](https://mlsanity.com/paper/1905.13538/) (2019)
- [Graph Convolutional Networks for Text Classification](https://mlsanity.com/paper/1809.05679/) (2018)
- [Image Generation from Scene Graphs](https://mlsanity.com/paper/1804.01622/) (2018)
- [Evaluation of Deep Convolutional Nets for Document Image Classification and Retrieval](https://mlsanity.com/paper/1502.07058/) (2015)
- [Constituency Parsing with a Self-Attentive Encoder](https://mlsanity.com/paper/1805.01052/) (2018)
- [DocBank: A Benchmark Dataset for Document Layout Analysis](https://mlsanity.com/paper/2006.01038/) (2020)
- [Graph Structured Network for Image-Text Matching](https://mlsanity.com/paper/2004.00277/) (2020)
- [Unified Pretraining Framework for Document Understanding](https://mlsanity.com/paper/2204.10939/) (2022)
- [Vision-and-Language Pretrained Models: A Survey](https://mlsanity.com/paper/2204.07356/) (2022)
- [Tensor Graph Convolutional Networks for Text Classification](https://mlsanity.com/paper/2001.05313/) (2020)
- [VSR: A Unified Framework for Document Layout Analysis combining Vision, Semantics and Relations](https://mlsanity.com/paper/2105.06220/) (2021)
- [Local Interpretations for Explainable Natural Language Processing: A Survey](https://mlsanity.com/paper/2103.11072/) (2021)
- [Cross-Domain Document Object Detection: Benchmark Suite and Method](https://mlsanity.com/paper/2003.13197/) (2020)
- [V-Doc : Visual questions answers with Documents](https://mlsanity.com/paper/2205.13724/) (2022)
- [Post-OCR Paragraph Recognition by Graph Convolutional Networks](https://mlsanity.com/paper/2101.12741/) (2021)
- [InducT-GCN: Inductive Graph Convolutional Networks for Text Classification](https://mlsanity.com/paper/2206.00265/) (2022)
- [VICTR: Visual Information Captured Text Representation for Text-to-Image Multimodal Tasks](https://mlsanity.com/paper/2010.03182/) (2020)
- [ME-GCN: Multi-dimensional Edge-Embedded Graph Convolutional Networks for Semi-supervised Text Classification](https://mlsanity.com/paper/2204.04618/) (2022)
- [Dating Documents using Graph Convolution Networks](https://mlsanity.com/paper/1902.00175/) (2019)
- [REXUP: I REason, I EXtract, I UPdate with Structured Compositional Reasoning for Visual Question Answering](https://mlsanity.com/paper/2007.13262/) (2020)

## Built on by (most cited first, 17 of 17)

- [A Survey on Knowledge-Oriented Retrieval-Augmented Generation](https://mlsanity.com/paper/2503.10677/) (2025)
- [PDFVQA: A New Dataset for Real-World VQA on PDF Documents](https://mlsanity.com/paper/2304.06447/) (2023)
- [Deep Learning based Visually Rich Document Content Understanding: A Survey](https://mlsanity.com/paper/2408.01287/) (2024)
- [Detect-Order-Construct: A Tree Construction based Approach for Hierarchical Document Structure Analysis](https://mlsanity.com/paper/2401.11874/) (2024)
- [Modeling Layout Reading Order as Ordering Relations for Visually-rich Document Understanding](https://mlsanity.com/paper/2409.19672/) (2024)
- [Docs2KG: Unified Knowledge Graph Construction from Heterogeneous Documents Assisted by Large Language Models](https://mlsanity.com/paper/2406.02962/) (2024)
- [SynJAC: Synthetic-data-driven Joint-granular Adaptation and Calibration for Domain Specific Scanned Document Key Information Extraction](https://mlsanity.com/paper/2410.01609/) (2024)
- [DocSAM: Unified Document Image Segmentation via Query Decomposition and Heterogeneous Mixed Learning](https://mlsanity.com/paper/2504.04085/) (2025)
- [3MVRD: Multimodal Multi-task Multi-teacher Visually-Rich Form Document Understanding](https://mlsanity.com/paper/2402.17983/) (2024)
- [PARAGRAPH2GRAPH: A GNN-based framework for layout paragraph analysis](https://mlsanity.com/paper/2304.11810/) (2023)
- [Dynamic Relation Transformer for Contextual Text Block Detection](https://mlsanity.com/paper/2401.09232/) (2024)
- [Enhancing Document Key Information Localization Through Data Augmentation](https://mlsanity.com/paper/2502.06132/) (2025)
- [PARL: Position-Aware Relation Learning Network for Document Layout Analysis](https://mlsanity.com/paper/2601.07620/) (2026)
- [Workshop on Document Intelligence Understanding](https://mlsanity.com/paper/2307.16369/) (2023)
- [Document AI: A Comparative Study of Transformer-Based, Graph-Based Models, and Convolutional Neural Networks For Document Layout Analysis](https://mlsanity.com/paper/2308.15517/) (2023)
- [LIGHT: Multi-Modal Text Linking on Historical Maps](https://mlsanity.com/paper/2506.22589/) (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)

## Abstract (arXiv)

Recognizing the layout of unstructured digital documents is crucial when parsing the documents into the structured, machine-readable format for downstream applications. Recent studies in Document Layout Analysis usually rely on computer vision models to understand documents while ignoring other information, such as context information or relation of document components, which are vital to capture. Our Doc-GCN presents an effective way to harmonize and integrate heterogeneous aspects for Document Layout Analysis. We first construct graphs to explicitly describe four main aspects, including syntactic, semantic, density, and appearance/visual information. Then, we apply graph convolutional networks for representing each aspect of information and use pooling to integrate them. Finally, we aggregate each aspect and feed them into 2-layer MLPs for document layout component classification. Our Doc-GCN achieves new state-of-the-art results in three widely used DLA datasets.