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The present work is concerned with handwritten and printed numeral recognition based on an improved version of the loci characteristic method (CL) for extracting the numeral features. After a preprocessing of the numeral image, the method divides the image into four equal parts and applies the traditional CL to each of the parts. The recognition rate obtained by this method is improved indicating that the numeral features extracted contain more details. Numeral recognition is carried out in this work through k nearest neighbors and multilayer perceptron techniques.

The fuzzy C-means algorithm is an unsupervised classification algorithm. This algorithm however, suffers from two difficulties which are the initialization phase and the local optimums. We present in this paper some improvements to this algorithm based on the evolutionary strategies in order to get around these two difficulties. We have designed a new evolutionist fuzzy C-means algorithm. We have proposed a new mutation operator in order for the algorithm to avoid local solutions and to converge to the global solution for a low computational time. This approach is validated on some simulation examples. The experimental results obtained confirm the rapidity of convergence and the good performances of the proposed algorithm.

We present in this work, a new unsupervised data classification technique based on a three steps system: Split, Clean and M erge. In this system, the classes are represented by a set of subclasses that we call prototypes. The prototypes are created in an incremental way from the initial data set. No prior knowledge on the classes is required. The data are presented to the system one by one in an arbitrary way. The system built on a neural network strategy ends up by acquiring knowledge on the data and gathers the data into a set of real classes which may have a non convex structure. The method proposed is compared to the fuzzy C-means 'FCM' and fuzzy min max clustering 'FMMC' methods through a number of simulations. The results obtained by the proposed method are very good.

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