ICDAR 2023 CROHME: Competition on Recognition of Handwritten Mathematical...
- zenodo.org
- data.niaid.nih.gov
zipUpdated Oct 10, 2023
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EmailClick to copy linkLink copiedCiteXIE Yejing; XIE Yejing; Mouchère Harold; Mouchère Harold; Simistira Liwicki Foteini; Simistira Liwicki Foteini; Rakesh Sumit; Saini Rajkumar; Nakagawa Masaki; Nakagawa Masaki; Nguyen Cuong Tuan; Nguyen Cuong Tuan; Truong Thanh-Nghia; Rakesh Sumit; Saini Rajkumar; Truong Thanh-Nghia (2023). ICDAR 2023 CROHME: Competition on Recognition of Handwritten Mathematical Expressions [Dataset]. http://doi.org/10.5281/zenodo.8428035zipAvailable download formatsUnique identifierhttps://doi.org/10.5281/zenodo.8428035Dataset updatedOct 10, 2023AuthorsXIE Yejing; XIE Yejing; Mouchère Harold; Mouchère Harold; Simistira Liwicki Foteini; Simistira Liwicki Foteini; Rakesh Sumit; Saini Rajkumar; Nakagawa Masaki; Nakagawa Masaki; Nguyen Cuong Tuan; Nguyen Cuong Tuan; Truong Thanh-Nghia; Rakesh Sumit; Saini Rajkumar; Truong Thanh-NghiaLicenseAttribution-NonCommercial-ShareAlike 3.0 (CC BY-NC-SA 3.0)https://creativecommons.org/licenses/by-nc-sa/3.0/
License information was derived automaticallyDescriptionHere is the datasets collected for the Competitionon Recognition of Online Handwritten Mathematical Expressions in competition session of ICDAR 2023.
3 tasks are proposed with different modalities, there are on-line, off-line and bi-modal.
For on-line task, we provide .inkml file (contain trace information, mathML and LaTeX string), and also symbol level label graph (SymLG) as ground truth. Except the new data and previous CROHME data, we also provide huge amount of artificial on-line data in the train set.
For off-line task, the .png images (scanned from paper or rendering from inkml) and symbol level label graph (SymLG) are provided. Except the new data and previous CROHME data, we use off-line images from OffHME to increase the size of train set.
For bi-modal task, both .inkml file and ,png images are provided as 2 channels input, and SymLG as ground truth.All the 3 tasks inherited the data collected from the previous 6 CROHME, and also the new collection 2023 in 3 sites, Nantes (France), Luleå (Sweden) and Tokyo (Japan).
CROHME2019
- kaggle.com
Updated Jun 3, 2024ShareFacebookTwitterEmailClick to copy linkLink copiedCiteCuong Nguyen (2024). CROHME2019 [Dataset]. https://www.kaggle.com/datasets/ntcuong2103/crohme2019/codeCroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.Dataset updatedJun 3, 2024AuthorsCuong NguyenLicenseAttribution-NonCommercial 4.0 (CC BY-NC 4.0)https://creativecommons.org/licenses/by-nc/4.0/
License information was derived automaticallyDescriptionICDAR 2019 Competition on Recognition of Handwritten Mathematical Expressions and Typeset Formula Detection (ICDAR2019-CROHME-TDF) - With temporal classification labeled data (generated from Label Graph)
\cite{Mouchère, ICDAR 2019 Competition on Recognition of Handwritten Mathematical Expressions and Typeset Formula Detection (ICDAR2019-CROHME-TDF) ,1,ID:ICDAR2019-CROHME-TDF_1,URL:https://tc11.cvc.uab.es/datasets/ICDAR2019-CROHME-TDF_1}
- h
CROHME-full
- huggingface.co
Updated Feb 20, 2025+ more versionsShareFacebookTwitterEmailClick to copy linkLink copiedCiteNam Nam (2025). CROHME-full [Dataset]. https://huggingface.co/datasets/Neeze/CROHME-fullCroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.Dataset updatedFeb 20, 2025AuthorsNam NamDescriptionNeeze/CROHME-full dataset hosted on Hugging Face and contributed by the HF Datasets community
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FacebookTwitterEmailAttribution-NonCommercial-ShareAlike 3.0 (CC BY-NC-SA 3.0)https://creativecommons.org/licenses/by-nc-sa/3.0/
License information was derived automatically
Here is the datasets collected for the Competitionon Recognition of Online Handwritten Mathematical Expressions in competition session of ICDAR 2023.
3 tasks are proposed with different modalities, there are on-line, off-line and bi-modal.
For on-line task, we provide .inkml file (contain trace information, mathML and LaTeX string), and also symbol level label graph (SymLG) as ground truth. Except the new data and previous CROHME data, we also provide huge amount of artificial on-line data in the train set.
For off-line task, the .png images (scanned from paper or rendering from inkml) and symbol level label graph (SymLG) are provided. Except the new data and previous CROHME data, we use off-line images from OffHME to increase the size of train set.
For bi-modal task, both .inkml file and ,png images are provided as 2 channels input, and SymLG as ground truth.
All the 3 tasks inherited the data collected from the previous 6 CROHME, and also the new collection 2023 in 3 sites, Nantes (France), Luleå (Sweden) and Tokyo (Japan).