Recognition of Degraded and Non Degraded Roman Characters Using Different Classifiers

Abstract

In this paper, we present an efficient and robust technique for the recognition of offline roman characters. The main strategy is to extract statistical and similarity features using a combination of grey level co-occurrence matrix (GLCM) and complementary similarity measure (CSM) method. In this work, the CSM method is used to extract features from binary images and combined with GLCM to boost the accuracy of character recognition. The recognition has been done using four different classifiers i.e. artificial neural network (ANN), Naive Bayes classifier, random forest (RF) and support vector machine (SVM). The standard dataset has been used for experimental work. We have done experiments on the clean and noisy dataset. It achieves the accuracy of 100% for some characters without noise and 94.11% with impulsive noise. A comparison of these four classifiers is recorded with and without a noisy environment. On a clean dataset, the random forest provides the best average recognition accuracy of 84.9% for all characters. On low noise datasets, random forest and artificial neural networks have almost the same recognition accuracy and on high noise datasets, SVM provides the highest recognition accuracy.