Normalisation of confidence voting methods applied to a fast handwritten OCR classification

Abstract

In this work, a normalisation of the weights utilized for combining classifiers decisions based on similarity Euclidean distance is presented. This normalisation is used by the confidence voting methods to decrease the final error rate in an OCR task. Different features from the characters are extracted. Each set of features is processed by a single classifier and then the decisions of the individual classifiers are combined using weighted votes, using different techniques. The error rates obtained are as good or slightly better than those obtained using a Freeman chain codes as contour representation and the string edit distance as similarity measure, but the complexity and classification time decrease dramatically.