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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 2 of 2 1 Unsupervised learning of morphology in the USSR Burlot, Franck; Yvon, François In: Journées internationales d'Analyse statistique des Données Textuelles ; https://hal.archives-ouvertes.fr/hal-01620908 ; Journées internationales d'Analyse statistique des Données Textuelles, Damon Mayaffre, Céline Poudat, Laurent Vanni, Véronique Magri, Peter Follette, Jun 2016, Nice, France (2016) BASE Show details 2 Automatic Handwritten Character Segmentation for Paleographical Character Shape Analysis Bluche, Théodore; Stutzmann, Dominique; Kermorvant, Christopher In: 2016 12th IAPR Workshop on Document Analysis Systems (DAS) ; https://hal.archives-ouvertes.fr/hal-02425715 ; 2016 12th IAPR Workshop on Document Analysis Systems (DAS), Apr 2016, Santorini, France. pp.42-47, ⟨10.1109/DAS.2016.74⟩ (2016) Abstract: International audience ; Written texts are both abstract and physical objects: ideas, signs and shapes, whose meanings, graphical systems and social connotations evolve through time. To study this dual nature of texts, paleographers need to analyse large scale corpora at the finest granularity, such as character shape. This goal can only be reached through an automatic segmentation process. In this paper, we present a method, based on Handwritten Text Recognition, to automatically align images of digitized manuscripts with texts from scholarly editions, at the levels of page, column, line, word, and character. It has been successfullyapplied to two datasets of medieval manuscripts, which are now almost fully segmented at character level. The quality of the word and character segmentations are evaluated and further paleographical analysis are presented. Keyword: [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]; [SHS.HIST]Humanities and Social Sciences/History; automatic text recognition; Character recognition; Error analysis; Hidden Markov models; Image segmentation; Paleography; Shape; Text recognition; Training; word and character segmentation URL: https://hal.archives-ouvertes.fr/hal-02425715 https://doi.org/10.1109/DAS.2016.74 BASE Hide details

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