Rough sets and fuzzy logic approach for handwritten digits and letters recognition

Abstract

This paper presents the hybrid approach using fuzzy logic and rough sets used as a pattern recognition framework. Both fuzzy and rough sets have been introduced to deal with vagueness and uncertain data in artificial intelligence applications. In general, fuzzy logic can be related to vagueness, while rough sets deal with indiscernibility. In our work we propose two-stage algorithm. At the first stage, an optimization procedure is applied to reduce the number of features for fuzzy membership functions and to find the optimal granulation for rough sets, respectively. In the second stage, two-step classifier is used. We tested our attempt using Handprinted Forms and Characters Database containing the full page binary images of 3699 handwriting sample forms. For any segmented image classification, a crucial part lies in the proper feature extraction method. In our work, cross corner feature algorithm was used as a main tool.