In this paper we present an alternative strategy to define zoning for handwriting recognition, which is based on nonsymmetrical perceptual zoning. The idea is to extract some knowledge from the confusion matrices in order to make the zoning process less empirical. The feature set considered in this work is based on concavities/convexities deficiencies, which are obtained by labeling the background pixels of the input image. To better assess the nonsymmetrical zoning we carried out experiments using four different zonings strategies. Experiments show that the nonsymmetrical zoning could be considered as a tool to build more reliable handwriting recognition systems.
We present a methodology to analyze Multiple Classifiers Systems (MCS) performance, using the disagreement concept. The goal is to define an alternative approach to the conventional recognition rate criterion, which usually requires an exhaustive combination search. This approach defines a Distance-based Disagreement (DbD) measure using an Euclidean distance computed between confusion matrices and a soft-correlation rule to indicate the most likely candidates to the best classifiers ensemble. As case study, we apply this strategy to two different handwritten recognition systems. Experimental results indicate that the method proposed can be used as a low-cost alternative to conventional approaches.
This paper presents a two-level based character recognition method in which a dynamically selection of the most promising zoning scheme for feature extraction allows us to obtain interesting results for character recognition. The first level consists of a conventional neural network and a look-up-table that is used to suggest the best zoning scheme for a given unknown character. The information provided by the first level drives the second level in the selection of the appropriate feature extraction method and the corresponding class-modular neural network. The experimental protocol has shown significant recognition rates for handwritten characters (from 80.82% to 88.13%).
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