This work presents a complete method for improving the handwritten document recognition. In this task some characters are confused with others because of their visual/structural similarity. A SOM and TreeSOM neural network were used to sort different characters in metaclasses. In each metaclass a zoning approach was applied trying to get particular features to improve the character classification. The experiments with this new approach were performed in the NIST database with the classic MLP and a fast neural network RBF-DDA.
I. INTRODUCTIONHE document recognition is an important intelligent systems research area. Commercially, several companies use this technology to solve complex real world activities. Character recognition systems are increasingly powerful for printed characters, however much remains to be improved in the handwritten recognition task.Analyzing the automatic handwritten recognition problem the major issue is the visual/structural similarities among some characters, e.g. the letters "I" and "J". The use of metaclass is a recognition approach to deals with this type of problem [1]. The metaclass approach builds clusters with similar characters and considers different characteristics using a local strategy to recognize each cluster of characters.The human strategy to character recognition task is similar to the metaclass approach, we first associates a well known part of the character and then a specialized recognition is used [2]. From this assumption many works have been proposed with a local processing strategy. The use of parts of the characters to extract some local features is called zoning. Some authors proposed empirical zoning [2] [3] and others an automatic zoning [4].In this paper we propose a new approach to build metaclasses. The metaclasses were created by SOM neural network [6]. The SOM technique allows the creation of clusters containing elements with similar characteristics. To find the best SOM cluster map we used the evaluation technique treeSOM [7]. So the clusters were built according to the best possible cluster composition.V. Macário is with the
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