Feature Extraction of Handwritten Symbols Using Fuzzy Logic
Feature extraction is a process whereby the input data is transformed into a set of features which characterise the input, and which can therefore be used to classify the input. This paper presents a new technique for feature extraction from handwritten symbols. We present a two-phase process. Firstly a pre-processing phase generates a chord vector for each handwritten stroke, thereby eliminating noise and greatly reducing the number of sections of the input which need to be assessed as potential features. Secondly fuzzy rules are used to determine membership values of chord sequences in fuzzy sets corresponding to feature types: Line, C-shape and O-shape. According to these membership values the most likely set of features is determined. Proper selection of the properties required for each feature type, and appropriate timing of application of the fuzzy rules, leads to an effective and efficient feature extraction algorithm.