Capsule Network on Font Style Classification

Abstract

Verification of font style followed in a file is a difficult task to classify. An artificial intelligence based algorithm network can effectively perform this task in reduced time. Capsule network is one among such algorithm and an emerging technique implemented for so many classification process with limited datasets. The proposed font style classification algorithm is enforced with Capsule Network (CapsNet) algorithm for executing the font style classification task. The proposed method is confirmed by classifying times new roman, Arial black and Algerian font style in English letters along with the performance evaluation in terms of accuracy and confusion matrix parameters. The proposed network structure is also compared with the existing Naive Bayes (NB), Decision Tree (DT) and K nearest neighbor (KNN) algorithms for comparative study and the evaluation result indicates that the proposed font style classification model based on CapsNet is classifying the images with better accuracy, F1 score and Gmean.