A Hybrid Method for Characters Recognition using Ant Colony Feature Selection, KNN and Reducts

This work addresses the development of a hybrid method for feature selection and a strategy to classify quite large datasets of handwritten characters. The divide and conquer paradigm, generally, is used to divide a big problem into minor problems. This research applied this concept to recognize handwritten uppercase letters and numbers. As a result, a big problem is split into two nodes or subproblems, one for numbers and one for letters. Then, letters are divided into two nodes representing the straight and curved ones. The division can be called the binary decision tree and allows to obtain a subset with the minimal features of each node called reduct. Here, an improvement of reducts is proposed using the ant colony algorithm as the embedded method. The application of these methods had the following result and conclusions. For each node, subsets of fewer features were obtained with high performance in the classification, considering the morphology of each letter. It is crucial to highlight that the distribution of the samples affects the performance of the classifier and the strategy improves the performance of the reduct.