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- W2578820368 abstract "A spiking neural network model for handwriting movement generation is proposed, in which curvilinear velocity signals are modeled with Beta profiles. In the trajectory domain each Beta profile is replaced by an elliptic arc to fit the initial stroke. The spiking neural network architecture is constituted by an input layer which uploads the Beta-elliptic parameters as input, hidden layer and the output layer where script coordinates X(t) and Y(t) are generated. A separate timing network prepares the initial state of the network. This latter involves the time-index starting time of each simple stroke for an appropriate handwriting movement signal. The experiments showed that our spiking neural network model could be applied for the both cases of Latin and Arabic handwriting scripts. Similarity degree is measured between original scripts and generated scripts to evaluate our model. New ways are proposed for the application of the spiking neural network model such as: generation of complex handwriting movements, signature verification and shape recognition." @default.
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- W2578820368 date "2016-10-01" @default.
- W2578820368 modified "2023-09-27" @default.
- W2578820368 title "A Spiking Motor-Model for Online Handwriting Movements Generation" @default.
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- W2578820368 doi "https://doi.org/10.1109/icfhr.2016.0094" @default.
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