{"url":"/dataset/im2latex-100k","name":"im2latex-100k","full_name":null,"description_markdown":"A prebuilt dataset for OpenAI's task for image-2-latex system. Includes total of ~100k formulas and images splitted into train, validation and test sets. Formulas were parsed from LaTeX sources provided here: http://www.cs.cornell.edu/projects/kddcup/datasets.html(originally from  arXiv)\r\n\r\nEach image is a PNG image of fixed size. Formula is in black and rest of the image is transparent.\r\n\r\nFor related tools (eg. tokenizer) check out this repository: https://github.com/Miffyli/im2latex-dataset\r\nFor pre-made evaluation scripts and built im2latex system check this repository: https://github.com/harvardnlp/im2markup\r\n\r\n**Newlines used in formulas_im2latex.lst are UNIX-style newlines (\\n). Reading file with other type of newlines results to slightly wrong amount of lines (104563 instead of 103558), and thus breaks the structure used by this dataset. Python 3.x reads files using newlines of the running system by default, and to avoid this file must be opened with newlines=\"\\n\" (eg. open(\"formulas_im2latex.lst\", newline=\"\\n\")).**\r\n\r\nSource: [https://zenodo.org/record/56198#.YJjuCGZKgox](https://zenodo.org/record/56198#.YJjuCGZKgox)\r\n\r\nImage source: [https://arxiv.org/pdf/1609.04938v2.pdf](https://arxiv.org/pdf/1609.04938v2.pdf)","description_withheld":null,"homepage":"https://zenodo.org/record/56198#.YJjuCGZKgox","introduced_date":"2016-09-16","introduced_date_note":null,"introduced_by":{"paper":"/paper/image-to-markup-generation-with-coarse-to","title":"Image-to-Markup Generation with Coarse-to-Fine Attention","first_author":"Yuntian Deng","url":null},"license":{"name":"CC0 1.0 Universal","url":"https://creativecommons.org/publicdomain/zero/1.0/legalcode"},"modalities":[],"tasks":[{"name":"Optical Character Recognition (OCR)","url":"/task/optical-character-recognition","datasets_with_task":"/datasets/task/optical-character-recognition"}],"languages":[],"variants":["im2latex-100k"],"data_loaders":[],"num_papers_in_archive":12,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/optical-character-recognition-on-im2latex-1","task":"Optical Character Recognition (OCR)","dataset_variant":"im2latex-100k","rows":1,"metrics":["BLEU"],"first_row_in_archive_order":{"model":"I2L-STRIPS","paper":"/paper/teaching-machines-to-code-neural-markup","metrics":{"BLEU":"88.86%"},"code_links":[{"title":"untrix/im2latex","url":"https://github.com/untrix/im2latex"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/teaching-machines-to-code-neural-markup","title":"Teaching Machines to Code: Neural Markup Generation with Visual Attention","date":"2018-02-15","rows_on_this_dataset":1,"code_links":1,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}