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--- |
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license: bsd-3-clause-clear |
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task_categories: |
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- image-to-text |
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--- |
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# LATTE: Improving Latex Recognition for Tables and Formulae with Iterative Refinement |
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Dataset artifact for paper, LATTE: Improving Latex Recognition for Tables and Formulae with Iterative Refinement (AAAI 2025) |
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Tab2Latex: a Latex table recognition dataset, with 87,513 training, 5,000 validation, and 5,000 test instances. |
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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. |
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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. |
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## Citation |
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``` |
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@article{jiang2025latte, |
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title = {LATTE: Improving Latex Recognition for Tables and Formulae with Iterative Refinement}, |
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author = {Jiang, Nan and Liang, Shanchao and Wang, Chengxiao and Wang, Jiannan and Tan, Lin}, |
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journal = {Proceedings of the AAAI Conference on Artificial Intelligence}, |
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volume = {39}, |
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number = {4}, |
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pages = {4030--4038}, |
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year = {2025}, |
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month = {Apr.}, |
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url = {https://ojs.aaai.org/index.php/AAAI/article/view/32422}, |
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doi = {10.1609/aaai.v39i4.32422}, |
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} |
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``` |
