Noise Removal in Binarized Handwritten Document Using Mamdani-type Fuzzy Inference Systems

Abstract

In this paper we present the processes of designing a Mamdani-type Fuzzy Inference Systems (FIS) for the detection and subsequent removal of small statistical and structural noise from a binarized handwritten document images. Features are extracted from the connected component followed by defining fuzzy sets on each shape feature. The number of fuzzy sets that are defined are dependent on the context knowledge and the rules that would be defined. The first step in the Mamdani’s Inference System which is referred to as fuzzification would then be to compute the degree of membership of each input variable x_i to all fuzzy sets that are defined on it. Then we construct the FIS systems so that they compute the degree of truth of a connected component being a dot, small noise, dash etc. based on the values of the features. The last step in the system is defuzzification; using Center of Gravity (COG) method of defuzzication for transforming a fuzzy set into a single crisp value. The research demonstrated the effectiveness of the rule-based noise removal system and how these rules can further be refined or expanded using more features.

Keywords: Fuzzy Inference System (FIS), Noise, Features, Image, Fuzzy Set