An Incremental and Hierarchical K-NN Classifier for Handwritten Characters

This paper analyses the application of hierarchical classifiers based on the k-NN rule to the automatic classification of handwritten characters. The discriminating capacity of a k-NN classifier increases as the size of the reference pattern set (RPS) increases. This supposes a problem for k-NN classifiers in real applications: the high computational cost required when the RPS is large. In order to accelerate the process of calculating the distance to each pattern of the RPS, some authors propose the use of condensing techniques. These methods try to reduce the size of the RPS without losing classification power. Our alternative proposal is based on incremental learning and hierarchical classifiers with rejection techniques that reduce the computational cost of the classifier. We have used 133,944 characters (72,105 upper-case characters and 61,839 lower-case characters) of the NIST Special Data Bases 3 and 7 as experimental data set. The binary image of the character is transformed to gray image. The best non-hierarchical classifier achieves a hit rate of 94.92% (upper-case) and 87,884% (lower-case). The hierarchical classifier achieves the same hit ratio, but with 3 times lower computational cost than the cost of the best non-hierarchical classifier found in our experimentation and 14% less than Hart’s Algorithm.