Éditeur(s) : HAL CCSD
Résumé : International audience
In this paper, we present a novel method for recognition of handwritten sketches. Unlike previous approaches, we focus on online retrieval and ability to build our model incrementally, thus we do not need to know all the data in advance and we can achieve very good recognition results after as few as 15 samples. The method is composed of two main parts: feature representation and learning and recognition. In feature representation part, we utilize SIFT-like feature descriptors in combination with soft response Bag-of-Words techniques. Descriptors are extracted locally using our novel sketch-specific sampling strategy and for support regions we follow patch-based approach. For learning and recognition, we use a novel technique based on fuzzy-neural networks, which has shown good performance in incremental learning. The experiments on state-of-the-art benchmarks have shown promising results.
17th Biennial Conference of the International Graphonomics Society
Pointe-à-Pitre, Guadeloupe

hal-01166497
https://hal.univ-antilles.fr/hal-01166497
https://hal.univ-antilles.fr/hal-01166497/document
https://hal.univ-antilles.fr/hal-01166497/file/IGS_2015_submission_37.pdf

Éditeur(s) : HAL CCSD
Résumé : International audience
Pen-based interfaces allow users to interact with the help of a stylus and/or fingers. Thanks to a recognition system, users can execute commands by drawing gestures. Because of the complexity of the software, the set of gesture commands can be very large. Consequently, it becomes difficult for the users to remember all the commands. In this paper we introduce "Semicustomizable gestural commands". The idea is to take into account the commands usage frequency: to facilitate the memorization of the most used commands we leave users the freedom/liberty to define their associated gestures (by) themselves. The remaining gesture commands will be automatically generated on the base of the defined gestures according to their hierarchical categorization. Several comparative tests demonstrate that this new approach improved the progressive memorization of the gestural commands.
ICFHR 2012 - International Conference on Frontiers in Handwriting Recognition
Bari, Italy

hal-00708750
https://hal.inria.fr/hal-00708750
https://hal.inria.fr/hal-00708750/document
https://hal.inria.fr/hal-00708750/file/ICFHR_LI_IEEE.pdf