Classification of handwritten digits using evolving fuzzy neural network

Handwritten digits classification has many useful applications. This has prompted decades of research into algorithms to produce an effective system of classifying handwritten images into text. Image processing and feature extraction play a large role in this process. An intelligent system is one, which is taught and uses its learning for classification effectively. The neuro-fuzzy model of evolving fuzzy neural network (EFuNN) is used for this purpose. This paper aims to analyse and obtain the optimal number of features that produces the most effective classification using EFuNN.