This paper presents a segmentation technique to handwritten word recognition. This technique implements an algorithm based on an analytical approach. It uses a letter sweeping procedure with a step equal to the Euclidean distance between an established reference index and the entity (the alphabet letter). Then a dissociation of this entity is achieved when this distance will reach a rate of 80%. Our experience about this segmentation technique gives a rate of 81.05% of recognition. A neural multi-layer perceptron classifier confirms the extracted segment. This procedure is successively repeated from the beginning until the end of the word. A concatenation technique is finally used to the word reconstitution.
Combining pattern recognition is a promising direction in designing effective classifiers. There are several approaches to collective decision-making, including quite popular voting methods where the decision is a combination of individual classifiers' outputs. The article focuses on the problem of fuser design which uses discriminants of individual classifiers to make a decision. We present taxonomy of proposed fusers and discuss some of their properties. We focus on the fuser which uses weights dependent on classifier and class number, because of a pretty low computational cost of its training. We formulate the problem of fuser learning as an optimization task and propose a solver which has its origin in neural computations. The quality of proposed learning algorithm was evaluated on the basis of several computer experiments, which were carried out on five benchmark datasets and their results confirm the quality of proposed concept.