Study on handwriting features of ambidextrous persons by using image processing

Abstract

Handwriting Features of Ambidextrous Persons have been classified as a multi-class classification issue in the deep learning framework, where image recognition task is effectively to test a classifier who can efficiently discriminate each line of paragraph of handwriting. This is very common for a single classifier to be performed on different data sets with a standard deep learning algorithm. It may also be that the same classifying system performs differently with highly differing image instances due to the various manuscript styles of various people on the same numbers. In order to address this problem, it was crucial to develop ensemble learning approaches to enhance overall effectiveness and make the performance highly stable in various data sets. In this text, we are proposing a system involving the CNN-based removal of the data set from an image and algebraic fusion of various words in images to classifies for trained in different sets of edges, which are prepared by means of feature selection applied to the original CNN feature set. Experimental results show that a fusion of classification images can achieve a descriptive word.