Datasets:
license: bsd-3-clause-clear
task_categories:
- image-to-text
LATTE: Improving Latex Recognition for Tables and Formulae with Iterative Refinement
Dataset artifact for paper, LATTE: Improving Latex Recognition for Tables and Formulae with Iterative Refinement (AAAI 2025)
Tab2Latex: a Latex table recognition dataset, with 87,513 training, 5,000 validation, and 5,000 test instances.
The LaTeX sources are collected from academic papers within these six distinct sub-fields of computer science—Artificial Intelligence, Computation and Language, Computer Vision and Pattern Recognition, Cryptography and Security, Programming Languages, and Software Engineering—from the arXiv repository, covering the years 2018 to 2023.
Once the paper sources are downloaded, tables are identified and extracted from the LaTeX source code by matching \begin{tabular} and \end{tabular} and removing the comments. Then, the LaTeX table source scripts are rendered to PDF format and converted to PNG format at 160 dpi.
Citation
@article{jiang2025latte,
title = {LATTE: Improving Latex Recognition for Tables and Formulae with Iterative Refinement},
author = {Jiang, Nan and Liang, Shanchao and Wang, Chengxiao and Wang, Jiannan and Tan, Lin},
journal = {Proceedings of the AAAI Conference on Artificial Intelligence},
volume = {39},
number = {4},
pages = {4030--4038},
year = {2025},
month = {Apr.},
url = {https://ojs.aaai.org/index.php/AAAI/article/view/32422},
doi = {10.1609/aaai.v39i4.32422},
}