Early Recognition of Handwritten Gestures based on Multi-classifier Reject Option

Zhaoxin Chen 1, 2, 3 Harold Mouchère 4, 2 Eric Anquetil 1, 3 Christian Viard-Gaudin 4, 2
1 INSA Rennes - Institut National des Sciences Appliquées - Rennes
2 IPI - Image Perception Interaction
LS2N - Laboratoire des Sciences du Numérique de Nantes
3 IntuiDoc - intuitive user interaction for document
IRISA-D6 - MEDIA ET INTERACTIONS
4 LS2N - Laboratoire des Sciences du Numérique de Nantes
Abstract : In this paper a multi-classifier method for early recognition of handwritten gesture is presented. Unlike the other works which study the early recognition problem related to the time, we propose to make the recognition according to the quantity of incremental drawing of handwritten gestures. We train a segment length based multi-classifier for the task of recognizing the handwritten touch gesture as early as possible. To deal with potential similar parts at the beginning of different gestures, we introduce a reject option to postpone the decision until ambiguity persists. We report results on two freely available datasets: MGSet and ILG. These results demonstrate the improvement we obtained by using the proposed reject option for the early recognition of handwritten gestures.
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Communication dans un congrès
14th IAPR International Conference on Document Analysis and Recognition (ICDAR2017), Nov 2017, Kyoto, Japan. 〈10.1109/ICDAR.2017.43〉
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Contributeur : Harold Mouchère <>
Soumis le : mardi 5 décembre 2017 - 16:31:39
Dernière modification le : mercredi 4 juillet 2018 - 10:20:04

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UNAM | UNIV-NANTES | INRIA | CENTRALESUPELEC | IRISA | INRIA2017 | LS2N | UR1-UFR-ISTIC | LS2N-IPI | IRISA-INSA-R | UNIV-RENNES

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Zhaoxin Chen, Harold Mouchère, Eric Anquetil, Christian Viard-Gaudin. Early Recognition of Handwritten Gestures based on Multi-classifier Reject Option. 14th IAPR International Conference on Document Analysis and Recognition (ICDAR2017), Nov 2017, Kyoto, Japan. 〈10.1109/ICDAR.2017.43〉. 〈hal-01653154〉

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