From Sign Recognition to Automatic Sign Language Understanding : Addressing the Non-Conventionalized Units

Autor: Belissen, Valentin

Publikováno v: Computer Vision and Pattern Recognition [cs.CV]. Université Paris-Saclay, 2020. English. ⟨NNT : 2020UPASG064⟩

Sign Languages (SLs) have developed naturally in Deaf communities. With no written form, they are oral languages, using the gestural channel for expression and the visual channel for reception. These poorly endowed languages do not meet with a broad

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=od_______212::d3b1cbe5e5033b8ee726c0606b701e48
https://tel.archives-ouvertes.fr/tel-03082011/document

Improving and Extending Continuous Sign Language Recognition: Taking Iconicity and Spatial Language into account

Autor: Belissen, Valentin, Gouiffès, Michèle, Braffort, Annelies

Publikováno v: LREC2020, 9th Workshop on the Representation and Processing of Sign Languages: Sign Language Resources in the Service of the Language Community, Technological Challenges and Application Perspectives
LREC2020, 9th Workshop on the Representation and Processing of Sign Languages: Sign Language Resources in the Service of the Language Community, Technological Challenges and Application Perspectives, E. Efthimiou, S.-E. Fotinea,T. Hanke, J. A. Hochgesang, J. Kristoffersen, J. Mesch (eds.), 2020, Marseille, France
Workshop on the Representation and Processing of Sign Languages
Workshop on the Representation and Processing of Sign Languages, E. Efthimiou, S.-E. Fotinea,T. Hanke, J. A. Hochgesang, J. Kristoffersen, J. Mesch (eds.), May 2020, Marseille, France

International audience; In a lot of recent research, attention has been drawn to recognizing sequences of lexical signs in continuous Sign Language corpora, often artificial. However, as SLs are structured through the use of space and iconicity, focu

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::b16440663306d484b691426918225632
https://hal.archives-ouvertes.fr/hal-02541816/document

Recurent neural network for handwriting recognition

Autor: Mioulet, Luc

Mass digitization of paper documents requires highly efficient optical cha-racter recognition systems. Digital versions of paper documents enable the useof search engines through keyword dectection or the extraction of high levelinformation (e.g. : t

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=od_______166::17e6b656811b1dc5250c181f8bd72734
https://hal.archives-ouvertes.fr/tel-01301728