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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. If you use this upload, please cite: Florence Cloppet, Véronique Eglin, Marlène Helias-Baron, van Cuong Kieu, Dominique Stutzmann, Nicole Vincent, "ICDAR 2017 Competition on the Classification of Medieval Handwritings in Latin Script", in 14th IAPR International Conference on Document Analysis and Recognition. ICDAR 2017, 1371-76. Kyoto: CPS, 2017. https://doi.org/10.1109/ICDAR.2017.224 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 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 representation. 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 https://clamm.irht.cnrs.fr/icdar-2017/ in Nov. 2017.
{"references": ["Florence Cloppet, V\u00e9ronique Eglin, Marl\u00e8ne Helias-Baron, van Cuong Kieu, Dominique Stutzmann, Nicole Vincent, \"ICDAR 2017 Competition on the Classification of Medieval Handwritings in Latin Script\", in 14th IAPR International Conference on Document Analysis and Recognition. ICDAR 2017, 1371-76. Kyoto: CPS, 2017. https://doi.org/10.1109/ICDAR.2017.224"]}
Image classification, Character recognition, Feature extraction, Medieval Latin script classification, Historical documents, Quantitative analysis
Image classification, Character recognition, Feature extraction, Medieval Latin script classification, Historical documents, Quantitative analysis
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