This paper deals with a recognition system of character for handwritten Tifinagh Text. Here in this work a neural network (the multi-layer perceptron MLP) and Hidden Markov Models (HMM) are proposed for handwritten characters identification. The features of Tifinagh characters are abstracted by mathematical morphology. Acquisition, scanning, thinning and text segmentation are also done in preprocessing phase before the classification with MLP and HMM. This work has achieved approximately 80% of success rate for Tifinagh handwritten text identification. General TermsHidden Markov Model HMM, Neural Network NN, Baum-Welch algorithm.

As classical methods are intractable for solving Markov decision processes (MDPs) requiring a large state space, decomposition and aggregation techniques are very useful to cope with large problems. These techniques are in general a special case of the classic Divide-and-Conquer framework to split a large, unwieldy problem into smaller components and solving the parts in order to construct the global solution. This paper reviews most of decomposition approaches encountered in the associated literature over the past two decades, weighing their pros and cons. We consider several categories of MDPs (average, discounted, and weighted MDPs), and we present briefly a variety of methodologies to find or approximate optimal strategies.

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Many hierarchical techniques to solve large Markov decision processes (MDPs) are based on the partition of the state space into strongly connected components (SCCs) that can be classified into some levels. In each level, smaller problems named restricted MDPs are solved, and then these partial solutions are combined to obtain the global solution. In this paper, we first propose a novel algorithm, which is a variant of Tarjan's algorithm that simultaneously finds the SCCs and their belonging levels. Second, a new definition of the restricted MDPs is presented to ameliorate some hierarchical solutions in discounted MDPs using value iteration (VI) algorithm based on a list of state-action successors. Finally, a robotic motion-planning example and the experiment results are presented to illustrate the benefit of the proposed decomposition algorithms.

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In this research, we present two comparative studies; the first one is between two methods of features extraction which are the mathematical morphology, the zoning and the hybridization of these two methods. The second comparative study is between both supervised methods used in learning-classification which are the Multi-Layer Perceptron (MLP) and the Support Vector Machines (SVM) applied to cursive handwritten Tifinagh characters recognition. The obtained experimental result demonstrates that the hybrid method is most efficient and the SVM is more performing than the MLP.

A review of literature shows that there is a variety of works studying coverage path planning in several autonomous robotic applications. In this work, we propose a new approach using Markov Decision Process to plan an optimum path to reach the general goal of exploring an unknown environment containing buried mines. This approach, called Goals to Goals Area Coverage on-line Algorithm, is based on a decomposition of the state space into smaller regions whose states are considered as goals with the same reward value, the reward value is decremented from one region to another according to the desired search mode. The numerical simulations show that our approach is promising for minimizing the necessary cost-energy to cover the entire area.