Fuzzy hyperline segment neural network for rotation invariant handwritten character recognition

In this paper fuzzy hyperline segment neural network (FHLSNN) is proposed which is used for recognition of handwritten characters. The FHLSNN utilizes fuzzy sets as pattern classes in which each fuzzy set is an union of fuzzy set hyperline segments. The fuzzy set hyperline segment is an n-dimensional hyperline segment defined by two end points with a corresponding membership function. The handwritten characters can be in arbitrary location, scale and orientation. After moment normalization rotation invariant ring-data and Zernike moment feature vectors are extracted from characters. Finally, FHLSNN algorithm is used to classify these feature vectors by its strong ability of discriminating ill-defined character classes. The FHLSNN algorithm is compared with fuzzy neural network proposed by Kwan and Cai (1994), which is modified to work under supervised environment and fuzzy min-max neural network proposed by Simpson (1992, 1993). The FHLSNN algorithm is found to be superior with respect to the training time, recall time per pattern and the generalization