# ICDAR 2021 Competition on Scientific Literature Parsing

arXiv: https://arxiv.org/abs/2106.14616 · HTML: https://mlsanity.com/paper/2106.14616/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jun 2021.

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

- [TableFormer: Table Structure Understanding with Transformers](https://mlsanity.com/paper/2203.01017/) (2022)
- [Document AI: Benchmarks, Models and Applications](https://mlsanity.com/paper/2111.08609/) (2021)
- [Improving Table Structure Recognition with Visual-Alignment Sequential Coordinate Modeling](https://mlsanity.com/paper/2303.06949/) (2023)
- [Robust Table Detection and Structure Recognition from Heterogeneous Document Images](https://mlsanity.com/paper/2203.09056/) (2022)
- [An End-to-End Multi-Task Learning Model for Image-based Table Recognition](https://mlsanity.com/paper/2303.08648/) (2023)
- [Rethinking Image-based Table Recognition Using Weakly Supervised Methods](https://mlsanity.com/paper/2303.07641/) (2023)
- [Graph Neural Networks and Representation Embedding for Table Extraction in PDF Documents](https://mlsanity.com/paper/2208.11203/) (2022)
- [Multi-Cell Decoder and Mutual Learning for Table Structure and Character Recognition](https://mlsanity.com/paper/2404.13268/) (2024)
- [SCORE: A Semantic Evaluation Framework for Generative Document Parsing](https://mlsanity.com/paper/2509.19345/) (2025)
- [High-Performance Transformers for Table Structure Recognition Need Early Convolutions](https://mlsanity.com/paper/2311.05565/) (2023)
- [AdaParse: An Adaptive Parallel PDF Parsing and Resource Scaling Engine](https://mlsanity.com/paper/2505.01435/) (2025)
- [Evaluating Table Structure Recognition: A New Perspective](https://mlsanity.com/paper/2208.00385/) (2022)
- [PdfTable: A Unified Toolkit for Deep Learning-Based Table Extraction](https://mlsanity.com/paper/2409.05125/) (2024)
- [Self-Supervised Pre-Training for Table Structure Recognition Transformer](https://mlsanity.com/paper/2402.15578/) (2024)
- [Hierarchical Modeling Approach to Fast and Accurate Table Recognition](https://mlsanity.com/paper/2512.21083/) (2025)
- [Revisiting Structural Dependency in Autoregressive Multi-Task Table Recognition via Order-Independent Cell-Level Representations](https://mlsanity.com/paper/2606.17874/) (2026)

## Abstract (arXiv)

Scientific literature contain important information related to cutting-edge innovations in diverse domains. Advances in natural language processing have been driving the fast development in automated information extraction from scientific literature. However, scientific literature is often available in unstructured PDF format. While PDF is great for preserving basic visual elements, such as characters, lines, shapes, etc., on a canvas for presentation to humans, automatic processing of the PDF format by machines presents many challenges. With over 2.5 trillion PDF documents in existence, these issues are prevalent in many other important application domains as well. Our ICDAR 2021 Scientific Literature Parsing Competition (ICDAR2021-SLP) aims to drive the advances specifically in document understanding. ICDAR2021-SLP leverages the PubLayNet and PubTabNet datasets, which provide hundreds of thousands of training and evaluation examples. In Task A, Document Layout Recognition, submissions with the highest performance combine object detection and specialised solutions for the different categories. In Task B, Table Recognition, top submissions rely on methods to identify table components and post-processing methods to generate the table structure and content. Results from both tasks show an impressive performance and opens the possibility for high performance practical applications.