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6 results for “script classification”

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zenodo40/100

ICFHR 2016 Competition on the Classification of Medieval Handwritings in Latin Script - Dataset

<p>The ICFHR2016 Competition on the Classification of Medieval Handwritings in Latin Script (CLaMM), jointly organized by Computer Scientists and Humanists (paleographers) provided a rich database of European medieval manuscripts to the community on Handwriting Analysis and Recognition.</p> <p>If you use this dataset, please cite:</p> <p>Florence Cloppet, V&eacute;ronique Eglin, Van Cuong Kieu, Dominique Stutzmann, and Nicole Vincent, &quot;ICFHR2016 Competition on the Classification of Medieval Handwritings in Latin Script&quot;, <em>Proceedings of International Conference on Frontiers in Handwriting Recognition</em>, Los Alamos : IEEE, 2016, p. 590-595. [<a href="https://doi.org/10.1109/ICFHR.2016.0113">https://doi.org/10.1109/ICFHR.2016.0113</a>]</p> <p>At this competition, we proposed two independent classification tasks which attracted five participants with seven submitted classifiers. Those classifiers are trained on a set of 2000 images with their ground truths. In the first task of script crisp classification, the classifiers have been evaluated on a test set of 1000 single-type manuscripts. In the second task of &ldquo;Fuzzy Classification&rdquo;, the classifiers have been carried out on a set of 2000 multi-script-type manuscripts.</p> <p>The present dataset contains the training dataset, both test datasets (task 1 and task 2) and the matrices provided by the competitors. It was first published on a https://clamm.irht.cnrs.fr/icfhr2016-clamm/ in Oct. 2016.</p> <p>&nbsp;</p>

opencc-by-4.0Oct 2016View details →
zenodo40/100

Script Classification and Writer Identification: Two Tasks for a Common Understanding of Cultural Heritage - Dataset

<p>Writer identification and Script classification are usually considered as two separated and very different tasks, as well in palaeography as in computer science. Following the ICDAR competition on the CLAMM corpus about script classification and dating, this dataset provides the output created by running two infrastructures created for Script classification at a large scale (medieval scripts) on a more homogeneous dataset with a focus on Writer Identification.</p> <p>If you use the present repository, its data and figures, please consult and cite:</p> <p>Stutzmann, Dominique, Christopher Tensmeyer, and Vincent Christlein. &laquo;&nbsp;Writer Identification and Script Classification: Two Tasks for a Common Understanding of Cultural Heritage&nbsp;&raquo;. manuscript cultures, 15 (2020): 11-24. <a href="https://www.csmc.uni-hamburg.de/publications/mc/files/articles/mc15-02-stutzmann.pdf">https://www.csmc.uni-hamburg.de/publications/mc/files/articles/mc15-02-stutzmann.pdf</a></p>

opencc-by-4.0Sep 2021View details →
zenodo32/100

dataset and script for app reviews classification

Open the record for dataset details and reuse information.

opencc-by-4.0Nov 2024View details →
zenodo32/100

ICDAR 2017 Competition on the Classification of Medieval Handwritings in Latin Script - Dataset

<p>The ICDAR2017 Competition on the Classification of Medieval Handwritings in Latin Script (CLaMM), jointly organized by Computer Scientists and Humanists (paleographers) followed a competition at ICFHR2016 and provided a rich annotated database of European medieval manuscripts to the community on Handwriting Analysis and Recognition, containing information on date of production and class of script.</p> <p>If you use this upload, please cite:</p> <p>Florence Cloppet, V&eacute;ronique Eglin, Marl&egrave;ne Helias-Baron, van Cuong Kieu, Dominique Stutzmann, Nicole Vincent, &quot;ICDAR 2017 Competition on the Classification of Medieval Handwritings in Latin Script&quot;, in <em>14th IAPR International Conference on Document Analysis and Recognition</em>. ICDAR 2017, 1371-76. Kyoto: CPS, 2017. <a href="https://doi.org/10.1109/ICDAR.2017.224">https://doi.org/10.1109/ICDAR.2017.224</a></p> <p>We proposed four independent classification tasks which attracted 10 registered teams, with 6 submitted classifiers from 4 participants. Those classifiers are trained on a set of 3540 images with their ground<br> truths. In task 1 (Script classification) and task 3 (Date classification), the classifiers have been evaluated by a test set of 2000 greyscale, tiff, 300 dpi images. In task 2 (Script classification) and task 4 (Date classification), the test set consists of 1000 images in different formats, resolutions and color<br> representation.</p> <p>The present dataset contains the training dataset, both test datasets (tasks 1 and 3, and tasks 2 and 4) and the matrices provided by the competitors. It was first published on <a href="https://clamm.irht.cnrs.fr/icdar-2017/">https://clamm.irht.cnrs.fr/icdar-2017/</a> in Nov. 2017.</p>

opencc-by-4.0Nov 2017View details →
dryad32/100

CryoSPARC scripts for oligomer classification and extraction of (pseudo)symmetric biomacromolecules

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publicSep 2025View details →
zenodo28/100

ICDAR 2021 Historical Document Classification Test Dataset for Task 1 - Scripts

<p>Test set for thescript classification task of the ICDAR 2021 Competition on Historical Document Classification competition. The tar.gz file contains the images and a ground truth CSV file. The csv file indicates for each image from which document and page it corresponds to, as well as whether augmentations have been applied.</p>

opencc-by-4.0May 2021View details →

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International Brain Laboratory public data

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Last verified 2026-04-29Open record