The Continuous Distance Transformation (CDT) used in conjunction with a k-NN classifier has been shown to provide good results in the task of handwriting recognition. Unfortunately, efficient techniques such as kd-tree search methods cannot be directly used in the case of certain dissimilarity measures like the CDT-based distance functions. In order to avoid exhaustive search, a simple methodology which combines fed-trees for fast search and Continuous Distance Transformation for fine classification, is presented. The experimental results obtained show that the recognition rates achieved have no significant differences with those found using an exhaustive CDT-based classification, with a very important temporal cost reduction.
展开▼
