Application d'un réseau de neurones ARTMAP à la reconnaissance des commandes gestuelles d'édition de documents braille

Applications crayon -- La tâche d'édition -- Commandes gestuelles -- Reconnaissance de commandes gestuelles -- Cadre fonctionnel de l'éditeur -- Analyse des besoins de l'édition gestuelle -- Plate-forme pour le développement d'applications à crayon et types d'applications à crayon -- La portée des reconnaisseurs -- Solution du problème de cohabitation entre le reconnaisseur local et le reconnaisseur du système -- Détails sur l'implantation de l'éditeur gestuel -- Le choix des gestes -- Revue des commandes gestuelles proposé.es par des chercheurs et des compagnies -- Sélection d'un ensemble de commandes gestuelles -- Le réseau ARTMAP pour la reconnaissance des commandes gestuelles -- Propriétés intéressantes des machines neuronales pour la reconnaissance des commandes gestuelles -- Le réseau ARTI -- Nature des données et prétraitement -- L'architecture ARTMAP -- la base de notre reconnaisseur de commandes gestuelles -- L'éditeur gestuel adaptable -- Les paramètres du reconnaisseur gestuel -- La création de la base de données

Contex-aware gestures for mixed-initiative text editings UIs

This is a pre-copyedited, author-produced PDF of an article accepted for publication in Interacting with computers following peer review. The version of record is available online at: http://dx.doi.org/10.1093/iwc/iwu019[EN] This work is focused on enhancing highly interactive text-editing applications with gestures. Concretely, we study Computer Assisted Transcription of Text Images (CATTI), a handwriting transcription system that follows a corrective feedback paradigm, where both the user and the system collaborate efficiently to produce a high-quality text transcription. CATTI-like applications demand fast and accurate gesture recognition, for which we observed that current gesture recognizers are not adequate enough. In response to this need we developed MinGestures, a parametric context-aware gesture recognizer. Our contributions include a number of stroke features for disambiguating copy-mark gestures from handwritten text, plus the integration of these gestures in a CATTI application. It becomes finally possible to create highly interactive stroke-based text-editing interfaces, without worrying to verify the user intent on-screen. We performed a formal evaluation with 22 e-pen users and 32 mouse users using a gesture vocabulary of 10 symbols. MinGestures achieved an outstanding accuracy (<1% error rate) with very high performance (<1 ms of recognition time). We then integrated MinGestures in a CATTI prototype and tested the performance of the interactive handwriting system when it is driven by gestures. Our results show that using gestures in interactive handwriting applications is both advantageous and convenient when gestures are simple but context-aware. Taken together, this work suggests that text-editing interfaces not only can be easily augmented with simple gestures, but also may substantially improve user productivity.This work has been supported by the European Commission through the 7th Framework Program (tranScriptorium: FP7- ICT-2011-9, project 600707 and CasMaCat: FP7-ICT-2011-7, project 287576). It has also been supported by the Spanish MINECO under grant TIN2012-37475-C02-01 (STraDa), and the Generalitat Valenciana under grant ISIC/2012/004 (AMIIS).Leiva, LA.; Alabau, V.; Romero Gómez, V.; Toselli, AH.; Vidal, E. (2015). Contex-aware gestures for mixed-initiative text editings UIs. Interacting with Computers. 27(6):675-696. https://doi.org/10.1093/iwc/iwu019S675696276Alabau V. Leiva L. A. Transcribing Handwritten Text Images with a Word Soup Game. Proc. Extended Abstr. Hum. Factors Comput. Syst. (CHI EA) 2012.Alabau V. Rodríguez-Ruiz L. Sanchis A. Martínez-Gómez P. Casacuberta F. 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Traitement en-ligne de documents manuscrits structurés : segmentation en mots par algorithmes d'apprentissage

Reconaissance de la structure physique -- Aperçu du système conçu et implanté -- Méthode de segmentation de textes manuscrits en mots -- Acquisition des données et extraction des caractéristiques pour la segmentation de textes en mots -- Classificateurs neuronaux pour la segmentation de textes manuscrits en mots -- Classificateurs basés sur l'algorithme d'AdaBoost