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Arabic character recognition is one of the most challenging tasks and exciting areas of research. In this one, we will present a mobile application for handwriting recognition. Our Mobile application proposes an approach for Arabic handwriting recognition based on the fact that handwriting is defined as a sequence of elementary and perceptual codes. After applying PerTOHS theory which is a perceptual...
AS-PSO-2Opt is a new enhancement of the AS-PSO method. In the classical AS-PSO, the Ant heuristic is used to optimize the tour length of a Traveling Salesman Problem, TSP, and PSO is applied to optimize three parameters of ACO, (α, β, ρ). The AS-PSO-2Opt consider a post processing resuming path redundancy, helping to improve local solutions and to decrease the probability of falling in local minimum...
Face Recognition is among the most widely studied problems in computer vision and Pattern Recognition. Face has many advantages like permanence, accessibility and universality. It is still now not solved in literature. Several approaches are proposed to overcome with problems including; changing posed, emotional states, and illumination variation, etc. Geometric approaches which used as example distance...
In this paper, a new Multi-Objective Particle Swarm Optimization (MOPSO) is applied to solve a problem of feature selection defined as a multiobjective problem. This algorithm (pMOPSO), known for its fast convergence with negligible computation time is based on a distributed architecture. Sub-swarms are obtained from dynamic subdivision of the population using Pareto Fronts. The algorithm addresses...
In this paper, we present a fuzzy genetic system for handwriting segmentation via perceptual codes based on assumptions of PerTOHS theory, which correspond to: handwriting is a form and a sequence of perceptual codes. Studying handwriting, we perceive the existence of elementary and global perceptual codes. Gathering the elementary ones in a choice of constraints we obtain global ones, and to obtain...
This paper proposes a learning method for Beta fuzzy systems (BFS) based on a multiagent genetic algorithm. This method, called Multi-Agent Genetic Algorithm for the Design of BFS has two advantages. First, thanks to genetic algorithms (GA) efficiency, it allows to design a suitable and precise model for BFS. Second, it improves the GA convergence by reducing rule complexity thanks to the distributed...
We propose an evolutionary method for the design of beta basis function neural networks (BBFNN) and of beta fuzzy systems (BFS). Classical training algorithms start with a predetermined network structure for neural networks and with a predetermined number of fuzzy rules for fuzzy systems. Generally speaking both the neural network and the fuzzy systems are either insufficient or overcomplicated. This...
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