# Post-OCR Paragraph Recognition by Graph Convolutional Networks

arXiv: https://arxiv.org/abs/2101.12741 · HTML: https://mlsanity.com/paper/2101.12741/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 49 references that are in the map.
- First posted on arXiv 29 Jan 2021.

## Builds on (most cited first, 17 of 17)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Neural Message Passing for Quantum Chemistry](https://mlsanity.com/paper/1704.01212/) (2017)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning](https://mlsanity.com/paper/1801.07606/) (2018)
- [Link Prediction Based on Graph Neural Networks](https://mlsanity.com/paper/1802.09691/) (2018)
- [Convolutional Networks on Graphs for Learning Molecular Fingerprints](https://mlsanity.com/paper/1509.09292/) (2015)
- [DeepGCNs: Can GCNs Go as Deep as CNNs?](https://mlsanity.com/paper/1904.03751/) (2019)
- [PubLayNet: largest dataset ever for document layout analysis](https://mlsanity.com/paper/1908.07836/) (2019)
- [Learning to Extract Semantic Structure from Documents Using Multimodal Fully Convolutional Neural Network](https://mlsanity.com/paper/1706.02337/) (2017)
- [Deep Relational Reasoning Graph Network for Arbitrary Shape Text Detection](https://mlsanity.com/paper/2003.07493/) (2020)
- [Deep Visual Template-Free Form Parsing](https://mlsanity.com/paper/1909.02576/) (2019)
- [Sequence-to-Label Script Identification for Multilingual OCR](https://mlsanity.com/paper/1708.04671/) (2017)

## Built on by (most cited first, 16 of 16)

- [Towards End-to-End Unified Scene Text Detection and Layout Analysis](https://mlsanity.com/paper/2203.15143/) (2022)
- [FormNet: Structural Encoding beyond Sequential Modeling in Form Document Information Extraction](https://mlsanity.com/paper/2203.08411/) (2022)
- [ICDAR 2023 Competition on Hierarchical Text Detection and Recognition](https://mlsanity.com/paper/2305.09750/) (2023)
- [ROPE: Reading Order Equivariant Positional Encoding for Graph-based Document Information Extraction](https://mlsanity.com/paper/2106.10786/) (2021)
- [Doc-GCN: Heterogeneous Graph Convolutional Networks for Document Layout Analysis](https://mlsanity.com/paper/2208.10970/) (2022)
- [Detect-Order-Construct: A Tree Construction based Approach for Hierarchical Document Structure Analysis](https://mlsanity.com/paper/2401.11874/) (2024)
- [DLAFormer: An End-to-End Transformer For Document Layout Analysis](https://mlsanity.com/paper/2405.11757/) (2024)
- [Hierarchical Text Spotter for Joint Text Spotting and Layout Analysis](https://mlsanity.com/paper/2310.17674/) (2023)
- [Unified Line and Paragraph Detection by Graph Convolutional Networks](https://mlsanity.com/paper/2203.09638/) (2022)
- [Text Reading Order in Uncontrolled Conditions by Sparse Graph Segmentation](https://mlsanity.com/paper/2305.02577/) (2023)
- [CTE: A Dataset for Contextualized Table Extraction](https://mlsanity.com/paper/2302.01451/) (2023)
- [PARAGRAPH2GRAPH: A GNN-based framework for layout paragraph analysis](https://mlsanity.com/paper/2304.11810/) (2023)
- [GVdoc: Graph-based Visual Document Classification](https://mlsanity.com/paper/2305.17219/) (2023)
- [Dynamic Relation Transformer for Contextual Text Block Detection](https://mlsanity.com/paper/2401.09232/) (2024)
- [Block-level Text Spotting with LLMs](https://mlsanity.com/paper/2406.13208/) (2024)
- [LIGHT: Multi-Modal Text Linking on Historical Maps](https://mlsanity.com/paper/2506.22589/) (2025)

## Abstract (arXiv)

We propose a new approach for paragraph recognition in document images by spatial graph convolutional networks (GCN) applied on OCR text boxes. Two steps, namely line splitting and line clustering, are performed to extract paragraphs from the lines in OCR results. Each step uses a beta-skeleton graph constructed from bounding boxes, where the graph edges provide efficient support for graph convolution operations. With only pure layout input features, the GCN model size is 3~4 orders of magnitude smaller compared to R-CNN based models, while achieving comparable or better accuracies on PubLayNet and other datasets. Furthermore, the GCN models show good generalization from synthetic training data to real-world images, and good adaptivity for variable document styles.