Éditeur(s) : HAL CCSD
Résumé : International audience
We propose an algorithm based on a model of visual perception that is meant to reflect the human judgment about the similarity of handwritten samples. The algorithm builds upon the Fuzzy Feature Contrast model and proposes an implementation of such a model in the domain of handwriting. The algorithm has been validated on the RIMES dataset, by comparing its performance with those of a panel of human experts. The experimental results show that the performance of the proposed algorithm is almost indistinguishable from the expert one and therefore may be a viable tool for handwriting comparison.
17th Biennial Conference of the International Graphonomics Society
Pointe-à-Pitre, Guadeloupe

hal-01165766
https://hal.univ-antilles.fr/hal-01165766
https://hal.univ-antilles.fr/hal-01165766/document
https://hal.univ-antilles.fr/hal-01165766/file/IGS_2015_submission_5.pdf

Éditeur(s) : HAL CCSD
Résumé : International audience
The large majority of methods proposed in literature for handwriting recognition assume that words are produced drawing large parts of the ink without lifting the pen, other than horizontal bars and dots. This fundamental assumption, however, does not always hold: while some educational systems provide explicit training for producing continuous handwriting, minimizing the number of pen-up during the production of a word, others do not. As a consequence, whenever the handwriting presents pen-up within a word, the recognition performance can drop significantly. In a preliminary study, we presented an algorithm for discriminating among different types of ink appearing in handwriting, namely isolated characters, cursive, dots, horizontal and vertical bars, based on the use of a suitable set of features. In this paper, we have characterized the discriminative power of each considered feature according to different measures and we have proposed a method for combining the different feature rankings. We have also used the Fischer's Linear Discriminant Analysis (LDA) for exhaustively selecting the best feature subsets with increasing number of features. Finally, we have compared the results obtained by using the feature subsets provided by LDA with those obtained with the feature subsets selected according to our feature ranking. The experimental results, on different datasets of handwritten words, showed that our approach successfully achieves its aim allowing to reduce the computational cost without affecting the overall performance of the recognition process.
17th Biennial Conference of the International Graphonomics Society
Pointe-à-Pitre, Guadeloupe

hal-01165877
https://hal.univ-antilles.fr/hal-01165877
https://hal.univ-antilles.fr/hal-01165877/document
https://hal.univ-antilles.fr/hal-01165877/file/IGS_2015_submission_13.pdf

Éditeur(s) : HAL CCSD
Résumé : International audience
We present a model of the spinal cord in controlling one degree-of-freedom arm movements. The model includes both neural and musculoskeletal functions in an integrated framework. The model has been implemented by an artificial neural network coupled with a computational model of muscle publicly available. The experimental results show that the model is able to regulate the position of the arm and to mediate reflex actions by integrating commands from CNS and signals from proprioceptors.
17th Biennial Conference of the International Graphonomics Society
Pointe-à-Pitre, Guadeloupe

hal-01165882
https://hal.univ-antilles.fr/hal-01165882
https://hal.univ-antilles.fr/hal-01165882/document
https://hal.univ-antilles.fr/hal-01165882/file/IGS_2015_submission_17.pdf