Feature extraction for neural-fuzzy inference system

Currently, not many attempts are made to use neural-fuzzy inference system for recognizing primitive features of an input image. The objective of this paper is to propose a method of feature extraction so as the features obtained can be trained in a novel neural-fuzzy inference system called POP-CHAR. Common features of digit characters are extracted and converted into vectors. The neural-fuzzy inference system can be trained from the primitive feature vectors and produce good results. Once the fuzzy neural network is trained, it can be used to recognize digits.