Learn More
This paper describes an off-line segmentation-free handwritten Arabic words recognition system. The described system uses discrete HMMs with explicit state duration of various kinds (Gauss, Poisson and Gamma) for the word classification purpose. After preprocessing, the word image is analyzed from right to left in order to extract from it a sequence of(More)
An incremental and Growing network model is introduced which is able to learn the topological relations in a given set of input vectors by means of a simple Hebb-like learning rule. First an overview of the most known models of Self-Organizing Maps (SOM) is given. Then we propose a new algorithm for a SOM which can learn new input data (plasticity) without(More)
We describe an offline unconstrained Arabic handwritten word recognition system based on segmentation-free approach and discrete hidden Markov models (HMMs) with explicit state duration. Character durations play a significant part in the recognition of cursive handwriting. The duration information is still mostly disregarded in HMM-based automatic cursive(More)
In this paper we present a system of the off-line handwriting recognition. Our recognition system is based on temporal order restoration of the off-line trajectory. For this task we use a genetic algorithm (GA) to optimize the sequences of handwritten strokes. To benefit from dynamic information we make a sampling operation by the consideration of(More)
He received a PhD degree from the University of Rouen in 1993 in the field of the cooperation in classification and neural networks for pattern recognition applications. His current research domain concerns problems with Learning, Classification, Data Analysis, and in particular, the problem of data incremental learning of neural networks. These activities(More)