Categorization of Text Documents Based on Fuzzy Attitude and Neural Networks

Abstract

With the exciting development of Internet and the increasing use of it for providing or acquiring information, we are witnessing an enormous volume of text documents and online images. This is considered as information redundancy, which is one of the prominent features of modern day life. In this regard, fast and accurate access to important and favorite resources is one of the concerns of users of these enormous resources of information. Today, what is of great importance is the lack of methods to find and optimally exploit the information available, rather than the shortage or lack of information. The problem with big image data, the effort to eliminate noise and visual disturbances such as parameters from inappropriate light sources, the inadequacy of color combinations, and many other factors in received images, are very important issues in working on images and processing them. In this regard, the method of classification of the texts from the images using a fuzzy system and neural network based algorithm is suggested. In this method, the location of the fuzzy system is introduced at the begin and end of the neural network synchronized with fuzzification operation and fuzzy inversion. In fact, the main idea in this article is to eliminate or minimize noise in classifying the documents with high inaccuracy.